1 Overview

Hu et al. in prep. Protistan grazing and biogeography at Gorda Ridge hydrothermal vent field

Code for all data analysis and figure generation, including grazing experiment analysis and sequence data processing. * Import raw counts from FLP disappearance experiments * Perform calculations to estimate grazing pressure * Generate figures to visualize grazing pressure * Import and quality control 18S and 16S tag-sequencing data * Taxonomy curation * Statistical analyses * Figure generation

2 Set up working R environment

The following analysis was performed in R version 3.6.1. All input files are available on the Gorda Ridge GitHub.

library(tidyverse)
library(reshape2)
library(cowplot)
library(patchwork)
library(phyloseq)
library(decontam)

3 Import grazing experiment results & quality control

# Metadata for each grazing experiment Including dive ID, vent/site name,
# incubation parameters
exp_list <- read.table("data-input/Table1_grazingexp_list.txt", header = T, fill = T, 
    sep = "\t")
# head(exp_list)

# Import all cell count information from FLP disappearance experiments
counts <- read.csv("data-input/GordaRidge-cell-count-results.csv")
counts_df <- counts %>% separate(Site, c("SampleOrigin", "SampleNumber", "Stain"), 
    "-", remove = FALSE) %>% separate(ID, c("TimePoint", "Bottle", "Replicate"), 
    "-", remove = FALSE) %>% add_column(excess = "NA108") %>% unite(Sample.ID, excess, 
    SampleNumber, sep = "-") %>% data.frame
# head(counts_df[1:3,])

# Import prok counts
prok <- read.table("data-input/prok_counts.txt", header = T, fill = T, sep = "\t")
head(prok[1:2, ])
##   Sample_Num Specific_Site Sample.location Prok_count   Vent.name
## 1  NA108-036      Plume036           Plume   76899.49 Mt Edwards2
## 2  NA108-013    Mt Edwards            Vent   76206.70  Mt Edwards

Sample Plume003, is more appropriately considered near vent bottom water. Modify sample name and entry below.

# Join count data with experiment IDs so each vent site can be identified by
# name:
counts_df_ids <- counts_df_mod %>% left_join(exp_IDs_mod, by = c(SampleOrigin = "Sample.Location", 
    Sample.ID = "Sample.ID")) %>% unite(Sample, TimePoint, Bottle, sep = "_", remove = FALSE) %>% 
    data.frame
## View combined table head(counts_df_ids)

3.1 Calculate error rate

Counts from Vent sample 110 T0 control were repeated three times (3 separate slides). Results are used below as technical replicates to estimate the percentage error rate.

By determining error rate from microscopy counting we can be more confident in evaluating true differences in values.

# Need to determine error rate across technical replicates.  Prepped a single
# sample 3 times (3 different days); this was counted separately to to estimate a
# personal error rate This is the % max and min that we will consider to be a
# margin of error
tech_check <- counts_df_ids %>% filter(Sample.ID %in% "NA108-110" & TimePoint %in% 
    "T0" & Bottle %in% "Ctrl" & !(Replicate %in% "R2")) %>% group_by(SampleOrigin, 
    Sample.ID) %>% summarise(MEAN = mean(Cellsperml), STDEV = sd(Cellsperml), ERR_PER = (100 * 
    (STDEV/MEAN))) %>% data.frame
## `summarise()` regrouping output by 'SampleOrigin' (override with `.groups` argument)
# head(tech_check)
PERCENT_ERR <- tech_check[["ERR_PER"]]
PERCENT_ERR  # Change in FLP time point to time point must exceed 16%
## [1] 16.14934

3.2 Estimate average cells/ml

Get average FLP concentration from T0 experiments and average cells/ml from proj counts

calc_FLP_avg <- counts_df_ids %>%
    group_by(SampleOrigin, Sample.ID, T, Bottle, Vent.name, Sample, Stain, T1, T2) %>%
    summarise(Avg_cellmL = mean(Cellsperml), # Average cells per ml across replicates
              sem=sd(Cellsperml)/sqrt(length(Cellsperml)), # Standard mean error
              SD=sd(Cellsperml),  #standard deviation
              var=sqrt(SD),  # variance
              Num = n()) %>% #Total number of 
    data.frame
## `summarise()` regrouping output by 'SampleOrigin', 'Sample.ID', 'T', 'Bottle', 'Vent.name', 'Sample', 'Stain', 'T1' (override with `.groups` argument)
# head(calc_FLP_avg)
# Separate T0 from other time points to calculate % differences in DTAF counts
# from T0 to T1 and T0 to T2
t0 <- filter(calc_FLP_avg, (T == "T0" & Stain == "DTAF")) %>% select(-T1, -T2, -Stain, 
    -Num, -T, -Sample, -SD, -var, Avg_cellmL_T0 = Avg_cellmL, sem_T0 = sem) %>% data.frame
# head(t0)

# Isolate non-T0 time points
t_ex <- filter(calc_FLP_avg, (!(T == "T0") & Stain == "DTAF")) %>% select(-Stain, 
    -Num, -Sample, -SD, -var) %>% pivot_wider(names_from = T, values_from = c(Avg_cellmL, 
    sem)) %>% data.frame
# head(t_ex) ?pivot_wider

bac_exp <- calc_FLP_avg %>% filter(Stain %in% "DAPI") %>% select(-Bottle, -Stain, 
    -T1, -T2, -SD, -var, -Num, bac_cellmL = Avg_cellmL, bac_sem = sem) %>% unite(SAMPLE, 
    SampleOrigin, Vent.name, sep = "-", remove = FALSE) %>% data.frame
# head(bac_exp)
dapi <- as.character(unique(bac_exp$SAMPLE))
# dapi

prok_avg <- prok_mod %>% group_by(Sample.location, Vent.name) %>% summarise(prok_avg = mean(Prok_count)) %>% 
    unite(SAMPLE, Sample.location, Vent.name, sep = "-", remove = FALSE) %>% data.frame
## `summarise()` regrouping output by 'Sample.location' (override with `.groups` argument)
# prok_avg

# Created: t0, t_ex, bac_exp, prok_avg
colnames(t0)
## [1] "SampleOrigin"  "Sample.ID"     "Bottle"        "Vent.name"    
## [5] "Avg_cellmL_T0" "sem_T0"

3.2.1 Find significant differences

Above data frame created lists the T0 FLP concentration and the T1 and T2 separately. The difference between T0 and T1 or T0 and T2 must exceed the percent error rate to be considered a reliable difference.

# Prep data frame to look at loss of FLP over time for all time points Compare to
# those that exceed error rate
PERCENT_ERR
## [1] 16.14934
cells_long <- flp_exp_summary %>% select(SAMPLE, Bottle, Vent.name, Avg_cellmL_T0, 
    Avg_cellmL_T1, Avg_cellmL_T2, T1, T2) %>% pivot_longer(cols = starts_with("Avg_cellmL"), 
    names_to = "CountID", values_to = "cellmL") %>% separate(CountID, c("avg", "excess", 
    "Tx"), sep = "_", remove = FALSE) %>% select(-avg, -excess) %>% data.frame

sem_long <- flp_exp_summary %>% select(SAMPLE, Bottle, Vent.name, sem_T0, sem_T1, 
    sem_T2) %>% pivot_longer(cols = starts_with("sem"), names_to = "semID", values_to = "sem") %>% 
    separate(semID, c("excess", "Tx"), sep = "_", remove = FALSE) %>% select(-excess) %>% 
    data.frame

# head(cells_long); head(sem_long)

# Combine and fix Timepoint
flp_long_toplot <- cells_long %>% left_join(sem_long) %>% select(-semID) %>% add_column(Hrs = 0) %>% 
    mutate(Hrs = case_when(Tx == "T1" ~ T1, Tx == "T2" ~ T2, TRUE ~ (as.integer(.$Hrs)))) %>% 
    select(-T1, -T2) %>% data.frame
## Joining, by = c("SAMPLE", "Bottle", "Vent.name", "Tx")
# head(flp_long_toplot)
## Plot average cells/ml for each experiment

# Factor for plotting
sample_order <- c("Near vent BW", "Mt Edwards", "Venti latte", "Candelabra", "SirVentsalot")
sample_label <- c("Near vent BW", "Mt. Edwards", "Venti latte", "Candelabra", "Sir Ventsalot")
sample_color <- c("#6f88af", "#61ac86", "#711518", "#dfa837", "#ce536b")
flp_long_toplot$SAMPLE_ORDER <- factor(flp_long_toplot$Vent.name, levels = (sample_order), 
    labels = sample_label)
names(sample_color) <- sample_label
bottle_order <- c("Ctrl", "Exp")
flp_long_toplot$BOTTLE <- factor(flp_long_toplot$Bottle, levels = bottle_order, labels = c("Control", 
    "Experimental"))
# svg('figs/Supplementary-FLP-CTRL-PercError-plot.svg', w = 7, h = 6)
ggplot(flp_long_toplot, aes(x = Hrs, y = cellmL, fill = SAMPLE_ORDER)) + geom_rect(data = (subset(flp_long_toplot, 
    Tx %in% "T0")), aes(xmin = 0, xmax = 40, ymin = (cellmL - ((PERCENT_ERR/100) * 
    cellmL)), ymax = (cellmL + ((PERCENT_ERR/100) * cellmL))), color = NA, alpha = 0.3) + 
    geom_line(stat = "identity", linetype = 1, aes(group = SAMPLE)) + geom_errorbar(aes(ymin = (cellmL - 
    sem), ymax = (cellmL + sem)), width = 0.1) + geom_point(stat = "identity", size = 3, 
    color = "black", aes(fill = SAMPLE_ORDER, shape = SAMPLE_ORDER)) + scale_y_log10() + 
    scale_fill_manual(values = sample_color) + scale_shape_manual(values = c(24, 
    21, 21, 21, 21)) + labs(y = bquote("FLP cells " ~ mL^-1), x = "Incubation hours") + 
    facet_grid(SAMPLE_ORDER ~ BOTTLE, scales = "free") + theme_bw() + theme(panel.grid.minor = element_blank(), 
    legend.title = element_blank(), strip.text.x = element_text(face = "bold", color = "black", 
        hjust = 0, size = 10), strip.text.y = element_text(size = 10), strip.background = element_blank(), 
    panel.background = element_blank(), panel.border = element_blank(), axis.line = element_line(colour = "black"), 
    axis.text = element_text(color = "black", size = 9))

# dev.off()

3.3 Refine FLP count results

Subset FLP results to select time points with significant loss in FLP/

# Subset Experiment results and filter for those that exceed the percent error
flp_sig <- flp_exp_summary %>%
    filter(Bottle %in% "Exp") %>%
    select(-T1, -T2) %>%
    mutate(T1_sig = case_when(
        T0_T1_PercDiff > PERCENT_ERR ~ "exceeds"),
           T2_sig = case_when(T0_T2_PercDiff > PERCENT_ERR ~ "exceeds")
          ) %>%
    data.frame

# head(flp_sig)

# Select experiments that T1 exceeds percent difference
T1_tmp <- flp_sig %>%
    filter(T1_sig == "exceeds") %>%
    select(SAMPLE) %>%
    data.frame
T1_tmp$Tx = "T1"
T1_tmp$Keep = "yes"

# Select experiments that T1 was NA, but T2 was significant
T2_tmp <- flp_sig %>%
    filter(is.na(T1_sig) & T2_sig == "exceeds") %>%
    select(SAMPLE) %>%
    data.frame
T2_tmp$Tx = "T2"
T2_tmp$Keep = "yes"

keep_status <- rbind(T1_tmp, T2_tmp); #keep_status
# # KEPT:
# # near vent point T2, Candelabra T2
# # Mt Edwards time point T1, Sirventsalot T1, & venti latte T1

These values are used for all downstream grazing rate calculations, as the loss in FLP was found to exceed the microscopy count error percentage.

# Factor for plotting use characterise lists from above
flp_trend_sig$SAMPLE_ORDER <- factor(flp_trend_sig$Vent.name, levels = (sample_order), 
    labels = sample_label)

plot_graze_trends <- ggplot(flp_trend_sig, aes(x = Hrs, y = cellmL, fill = SAMPLE_ORDER, 
    shape = SampleOrigin)) + geom_line(stat = "identity", aes(group = SAMPLE_ORDER, 
    linetype = SampleOrigin)) + geom_errorbar(aes(ymin = (cellmL - sem), ymax = (cellmL + 
    sem)), size = 0.5, width = 0.1) + geom_point(stat = "identity", size = 3, color = "black") + 
    scale_linetype_manual(values = c(1, 1)) + scale_fill_manual(values = sample_color) + 
    scale_shape_manual(values = c(23, 21)) + scale_y_log10(limits = c(5000, 1e+05)) + 
    labs(y = bquote("FLP cells " ~ mL^-1), x = "Incubation hours") + theme_minimal() + 
    theme(panel.grid.major = element_line(), panel.grid.minor = element_blank(), 
        panel.background = element_blank(), axis.line = element_line(colour = "black"), 
        axis.text = element_text(color = "black"), legend.title = element_blank()) + 
    guides(fill = guide_legend(override.aes = list(shape = c(23, 21, 21, 21, 21))), 
        shape = guide_legend(override.aes = list(fill = "black"))) + annotation_logticks(sides = "l")
# plot_graze_trends

Consistent loss in FLP over time

3.4 Calculate mortality and grazing rate

Grazing rate calculation from Connell et al. 2017 mortality factor = ln(Tf/T0) * (-1/t) t = incubation hours reported as days Tf = number of FLP at end of experiment T0 = number of FLP at beginning of experiment The natural log in R is ‘log()’

# cellmL = prokaryote average cells per ml
graze_rate <- processed_data %>%
    # type.convert(as.is = TRUE) %>%
    group_by(SAMPLE, SampleOrigin, Vent.name, Hrs_Tf, SAMPLE_ORDER) %>%
    mutate(
        # Calculate mortality factor (m)
          MORTALITY = (log(cellmL_Tf/cellmL_T0))*(-1/(Hrs_Tf/24)),
           MORTALITY_min = (log((cellmL_Tf-sem_Tf)/(cellmL_T0-sem_T0)))*(-1/(Hrs_Tf/24)),
           MORTALITY_max = (log((cellmL_Tf+sem_Tf)/(cellmL_T0+sem_T0)))*(-1/(Hrs_Tf/24)),
           # Calculate model I G - Rate over given amount of time
           G = ((cellmL_T0 - cellmL_Tf) * (prok_avg / cellmL_T0)),
           G_min = (((cellmL_T0-sem_T0) - (cellmL_Tf-sem_Tf)) * (prok_avg / (cellmL_T0-sem_T0))),
           G_max = (((cellmL_T0+sem_T0) - (cellmL_Tf+sem_Tf)) * (prok_avg / (cellmL_T0+sem_T0))),
           # Calculate Grazing per hour
           GrazingRate_hr = (G/Hrs_Tf), 
           GrazingRate_hr_min = (G_min/Hrs_Tf),
           GrazingRate_hr_max = (G_max/Hrs_Tf),
           # Estimate prokaryote turnover % per day
           Prok_turnover = (100*(G / prok_avg)), #Convert to per day (*24)
           Prok_turnover_min = (100*(G_min / prok_avg)),
           Prok_turnover_max = (100*(G_max / prok_avg)),
           # Prok_turnover = (100*((rate * cellmL)/cellmL)), #ARCHIVE
           # Prok_turnover_min = (100*((rate_min * cellmL)/cellmL)), #ARCHIVE
           # Prok_turnover_max = (100*((rate_max * cellmL)/cellmL)) #ARCHIVE
           # Model II
           N_avg = ((prok_avg + prok_avg)/2),
           F_avg = ((cellmL_T0 + cellmL_Tf)/2),
           q = ((cellmL_T0 - cellmL_Tf)/F_avg),
           # G_II a and b should be equivalent
           G_II_a = q * (N_avg),
           G_II_b = ((cellmL_T0 - cellmL_Tf) * ((prok_avg+prok_avg)/(cellmL_T0+cellmL_Tf))),
           GrazingRate_hr_II = (G_II_a/Hrs_Tf)
           ) %>%
        data.frame
# Factor for plotting
sample_order <- c("Near vent BW", "Mt Edwards", "Venti latte", "Candelabra", "SirVentsalot")
sample_label <- c("Near vent BW", "Mt. Edwards", "Venti latte", "Candelabra", "Sir Ventsalot")
sample_color <- c("#6f88af", "#61ac86", "#711518", "#dfa837", "#ce536b")

graze_rate$SAMPLE_ORDER <- factor(graze_rate$Vent.name, levels = rev(sample_order), 
    labels = rev(sample_label))

mortality <- ggplot(graze_rate, aes(x = SAMPLE_ORDER, y = GrazingRate_hr, fill = SAMPLE_ORDER, 
    shape = SampleOrigin)) + geom_errorbar(aes(ymin = GrazingRate_hr_min, ymax = GrazingRate_hr_max), 
    size = 0.5, width = 0.1) + geom_point(stat = "identity", size = 3, color = "black", 
    aes(shape = SampleOrigin)) + scale_fill_manual(values = rev(sample_color)) + 
    scale_shape_manual(values = c(23, 21)) + coord_flip() + labs(x = "", y = bquote("Cells " ~ 
    mL^-1 ~ consumed ~ hr^-1)) + theme_minimal() + theme(panel.grid.major = element_line(), 
    panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black"), 
    axis.text = element_text(color = "black"), axis.ticks = element_line(), legend.position = "none", 
    strip.text = element_blank())

# mortality
bar_plot <- ggplot(graze_rate, aes(x = SAMPLE_ORDER, y = Prok_turnover)) + geom_bar(stat = "identity", 
    position = "stack", width = 0.6, aes(fill = SAMPLE_ORDER)) + geom_errorbar(aes(ymin = Prok_turnover_min, 
    ymax = Prok_turnover_max), size = 0.5, width = 0.1) + scale_fill_manual(values = rev(sample_color)) + 
    scale_y_continuous(expand = c(0, 0), limits = c(0, 100)) + labs(x = "", y = bquote("Prokaryote turnover %" ~ 
    d^-1)) + coord_flip() + theme_minimal() + theme(panel.grid.major = element_line(), 
    panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black"), 
    axis.text = element_text(color = "black"), axis.ticks = element_line(), legend.position = "none", 
    strip.text = element_blank())
# bar_plot

3.4.1 Final figure generation

leg <- get_legend(plot_graze_trends)

# svg("figs/Grazing-results-panel-VERT-23-12-2020.svg", h = 8, w = 3.2)
plot_grid(plot_graze_trends + theme(legend.position = "none"),
          mortality,
          bar_plot,
          # mortality + theme(axis.text.y = element_blank()),
          # bar_plot + theme(axis.text.y = element_blank()), #leg,
          axis = c("lrtb"), align = c("hv"), labels = c("a", "b", "c", ""), nrow = 3, ncol = 1)

# dev.off()

3.5 Estimated carbon calculations

# Replace with the calcs to place into context with McNichol et al. work
# head(graze_rate)
# G = number of cells grazed during experiment duration
graze_rate_wCarbon <- graze_rate %>%
  add_column(fgC_cell_morono = 86) %>% # Add in Morono et al. 2011 value
  add_column(fgC_cell_mcnic = 173) %>% 
  mutate(
    cells_consumed_perday = (G / (Hrs_Tf /24)), # Rate of cells consumed * in situ prok, per day (day = hours of incubation reported in days)
    fgC_ml_perday_morono = (cells_consumed_perday * fgC_cell_morono),
    fgC_ml_perday_mcnic = (cells_consumed_perday * fgC_cell_mcnic),# Convert cell amount to fg C
    ugC_L_perday_morono = (fgC_ml_perday_morono * (1e-09) * 1000), # Convert to ug C per L
    ugC_L_perday_mcnic = (fgC_ml_perday_mcnic * (1e-09) * 1000),
    lower_mcnichol_morono = 100*(ugC_L_perday_morono / 17.3),
    upper_mcnichol_morono = 100*(ugC_L_perday_morono / 321.4),
    lower_mcnichol_mcnic = 100*(ugC_L_perday_mcnic / 17.3),
    upper_mcnichol_mcnic = 100*(ugC_L_perday_mcnic / 321.4)
  ) %>% 
  data.frame
# View(graze_rate_wCarbon)
# write_delim(graze_rate_wCarbon, path = "Grazing-calc-wCarbon-results.txt", delim = "\t")

4 Import 18S ASV table & quality control

Set up working R environment and import 18S ASV table. Modify input tables and import as phyloseq objects in order to perform quality control removal of contaminant ASVs (decontam).

load("data-input/GR-ASVtables-updatedTax.RData", verbose = TRUE)
## Loading objects:
##   GR_tagseq_longformat
##   GR_tagseq_wideformat

4.1 Clean ASV table with ‘decontam’

Import ASV table as phyloseq object, note control samples.

taxmat <- GR_tagseq_wideformat %>% select(Feature.ID, Taxon_updated) %>% separate(Taxon_updated, 
    c("Kingdom", "Supergroup", "Division", "Class", "Order", "Family", "Genus", "Species"), 
    sep = ";", remove = FALSE) %>% column_to_rownames(var = "Feature.ID") %>% as.matrix
# class(taxmat) head(taxmat)

Note that Axial ID originates from a laboratory blank sample that was exactrated at the same time.

asvmat <- GR_tagseq_wideformat %>% select(Feature.ID, starts_with(c("Gorda", "Axial"))) %>% 
    column_to_rownames(var = "Feature.ID") %>% as.matrix

Import metadata below and combine with phyloseq object.

##                               SAMPLE       LOCATION LOCATION_SPECIFIC SAMPLEID
## 1     Axial_ExtractControl_CTRL_2019 ExtractControl    ExtractControl     CTRL
## 2  GordaRidge_Plume001_T0_2019_REP12     GordaRidge          Plume001       T0
## 3 GordaRidge_Plume001_T24_2019_REP12     GordaRidge          Plume001      T24
## 4 GordaRidge_Plume001_T36_2019_REP12     GordaRidge          Plume001      T36
## 5   GordaRidge_Vent013_T0_2019_REP13     GordaRidge           Vent013       T0
## 6  GordaRidge_Vent013_T36_2019_REP12     GordaRidge           Vent013      T36
##   Sampletype    LocationName Sample_or_Control Sample_or_BSW
## 1    Control       Lab blank    Control Sample       Control
## 2    Grazing    Near vent BW       True Sample   True Sample
## 3    Grazing    Near vent BW       True Sample   True Sample
## 4    Grazing    Near vent BW       True Sample   True Sample
## 5    Grazing Mt Edwards Vent       True Sample   True Sample
## 6    Grazing Mt Edwards Vent       True Sample   True Sample

4.2 Identify contaminant ASVs

Decontam will identify putative contaminate ASVs based on the difference in prevalence between control blank and environmental samples. First review the library size or number of sequences within each sample to see how varied the control samples are to the experimental samples.

# Decontam:
physeq_names
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 9175 taxa and 34 samples ]
## sample_data() Sample Data:       [ 34 samples by 9 sample variables ]
## tax_table()   Taxonomy Table:    [ 9175 taxa by 9 taxonomic ranks ]
# Check out library size of my data
df <- as.data.frame(sample_data(physeq_names))
df$LibrarySize <- sample_sums(physeq_names)
df <- df[order(df$LibrarySize), ]
df$Index <- seq(nrow(df))
# 
ggplot(data = df, aes(x = Index, y = LibrarySize, fill = Sample_or_Control, shape = LOCATION)) + 
    geom_point(color = "black", size = 3, aes(shape = LOCATION)) + scale_shape_manual(values = c(21, 
    22, 23)) + theme_bw()

> Shows that out of the 3 ship blanks I have, one of the sames has a pretty large library size, otherwise, control samples have very small library sizes.

# Assign negative control designation
sample_data(physeq_names)$is.neg <- sample_data(physeq_names)$Sample_or_Control == 
    "Control Sample"

# ID contaminants using Prevalence information
contamdf.prev <- isContaminant(physeq_names, method = "prevalence", neg = "is.neg", 
    threshold = 0.5, normalize = TRUE)
table(contamdf.prev$contaminant)  # Report number of ASVs IDed as contamintants
## 
## FALSE  TRUE 
##  9141    34

0.5 - this threshold will ID contaminants in all samples that are more prevalent in negative controls than in positive samples. In this study, control samples included 1 lab-based blank and 3 ship-board blanks taken at the time of field study. Results showed 34 ASVs to be considered “contaminants”

# Make phyloseq object of presence-absence in negative controls and true samples
# change to presence absence
gr.pa <- transform_sample_counts(physeq_names, function(abund) 1 * (abund > 0))

# isolate PA of positive and negative samples
gr.pa.neg <- prune_samples(sample_data(gr.pa)$Sample_or_Control == "Control Sample", 
    gr.pa)
gr.pa.pos <- prune_samples(sample_data(gr.pa)$Sample_or_Control == "True Sample", 
    gr.pa)

4.3 Remove contaminant ASVs from data

# Subset TRUE contaminants
contams <- subset(contamdf.prev, contaminant == "TRUE")
contams$Feature.ID <- row.names(contams)
# head(contams);dim(contams)
list_of_contams <- as.character(contams$Feature.ID)
# Explore taxa IDed as contaminants
taxa_list <- as.data.frame(taxmat)
taxa_list$Feature.ID <- row.names(taxa_list)

taxa_contams <- left_join(contams, taxa_list)
# write_delim(taxa_contams, path = 'List-of-contaminant-ASVs.txt', delim = '\t')

# Plot total sequences and which are contaminants Remove contaminant and count
# sequence sums per sample to see which samples had the highest number of
# contamiant sequences removed.  After remove contaminants, what % of sequences
# is removed?  head(GR_tagseq_counts[1:2,])
GR_tagseq_longformat$CONTAM <- "Pass"
# head(contams[1:2,]) str(list_of_contams)
GR_tagseq_longformat$CONTAM[GR_tagseq_longformat$Feature.ID %in% list_of_contams] = "Fail"
# head(GR_tagseq_counts[1:2,])

# Make character list of all feature.ids to KEEP:
keep1 <- subset(GR_tagseq_longformat, CONTAM %in% "Pass")
# length(unique(keep1$Feature.ID))
keep_asvs <- as.character(unique(keep1$Feature.ID))  #see below
# 
passfail <- GR_tagseq_longformat %>% group_by(SAMPLE, CONTAM) %>% summarise(SUM_CONTAM = sum(COUNT)) %>% 
    data.frame

4.4 Report sequence stats

passfail_wID <- left_join(passfail, ventnames, by = "SAMPLE")
# Original number of reads
sum(GR_tagseq_longformat$COUNT)
## [1] 1569829
# Original number of ASVs
length(unique(GR_tagseq_longformat$Feature.ID))
## [1] 9175
# unique(GR_tagseq_counts$SAMPLEID)
GR_tagseq_counts_noCTRL <- subset(GR_tagseq_longformat, !(SAMPLEID %in% "CTRL"))
# New total number of sequences
sum(GR_tagseq_counts_noCTRL$COUNT)
## [1] 1479273
counts_decont <- subset(GR_tagseq_longformat, !(Feature.ID %in% list_of_contams))
length(unique(counts_decont$Feature.ID)) - length(unique(GR_tagseq_longformat$Feature.ID))  # Confirm 34 lines removed
## [1] -34
# % of sequences was removed following decontam; this is counting the ship blank
# samples themselves
100 * (1 - (sum(counts_decont$COUNT)/sum(GR_tagseq_counts_noCTRL$COUNT)))
## [1] 1.23581
# head(counts_decont)
# Breakdown by samples:
passfail_wide <- dcast(passfail, SAMPLE ~ CONTAM)
passfail_wide$PercLossSeq <- paste(100 * (passfail_wide$Fail/(passfail_wide$Fail + 
    passfail_wide$Pass)))
# dim(passfail_wide) write.csv(passfail_wide, file='PercSeqLost-decontam.csv')
# breakdown by sample - reports % lost per sample

# Remove contaminant sequences from phyloseq object: Subset TRUE contaminants
# ?prune_taxa class(keep_asvs)
physeq_tmp <- prune_taxa(keep_asvs, physeq_names)
# sample_data(physeq_tmp)

# Remove one sample with too few sequences
physeq_clean <- subset_samples(physeq_tmp, sample_names(physeq_tmp) != "GordaRidge_BSW020_sterivex_2019_REPa")
# sample_data(physeq_clean) physeq_clean Remove control samples from data frame
tmp <- subset(GR_tagseq_longformat, !(SAMPLEID %in% "CTRL"))  # Remove controls, get list of sample names that are controls
samples_keep <- as.character(unique(tmp$SAMPLE))
physeq_clean_true <- prune_samples(samples_keep, physeq_clean)

4.5 Save output

# Save as R Data save(counts_decont,
# file='data-input/GR-ASV-table-clean-19-08-2020.RData')

5 Plot high level taxonomy of protists in situ

Import cleaned ASV data, curate taxonomic assignments specific to protists, create bar plot to demonstrate protistan diversity at Gorda Ridge.

load("data-input/GR-ASV-table-clean-19-08-2020.RData", verbose = TRUE)  # after decontam clenaing
## Loading objects:
##   counts_decont
gr_counts <- counts_decont %>% filter(COUNT > 0) %>% separate(Taxon_updated, c("Kingdom", 
    "Supergroup", "Division", "Class", "Order", "Family", "Genus", "Species"), sep = ";", 
    remove = FALSE) %>% data.frame
# head(gr_counts)
tax_only_tmp <- gr_counts %>% select(Taxon_updated, Kingdom, Supergroup, Division, 
    Class, Order, Family, Genus, Species) %>% distinct() %>% data.frame
ventnames <- read.delim("data-input/ventnames-gordaridge.txt")
colnames(ventnames)[1] <- "SAMPLE"

# Join with dataframe
gr_counts_name <- gr_counts %>% left_join(select(ventnames, SAMPLE, LOCATION_SPECIFIC, 
    Sampletype, LocationName)) %>% data.frame

gr_counts_name$LocationName[gr_counts_name$LOCATION == "Shipblank"] = "Shipblank"

5.1 Taxonomy curation - PR2

Function below pr2_curate() is the custom manual curation of the taxonomic assignments from the PR2 database. The function creates new columns with taxonomic information that summarizes the core groups in the dataset.

pr2_curate <- function(df) {
    # Add a column
    df$Taxa <- "Unassigned-Eukaryote"
    df$Taxa[df$Supergroup == "Alveolata"] = "Alveolata-Other"
    df$Taxa[df$Division == "Ciliophora"] = "Alveolata-Ciliates"
    df$Taxa[df$Division == "Dinoflagellata"] = "Alveolata-Dinoflagellates"
    df$Taxa[df$Class == "Syndiniales"] = "Alveolata-Syndiniales"
    df$Taxa[df$Class == "Apicomplexa"] = "Alveolata-Apicomplexa"
    df$Taxa[df$Supergroup == "Hacrobia"] = "Hacrobia-Other"
    df$Taxa[df$Division == "Cryptophyta"] = "Hacrobia-Cryptophyta"
    df$Taxa[df$Division == "Haptophyta"] = "Hacrobia-Haptophyta"
    df$Taxa[df$Supergroup == "Opisthokonta"] = "Opisthokonta-Other"
    df$Taxa[df$Division == "Fungi"] = "Opisthokonta-Fungi"
    df$Taxa[df$Division == "Metazoa"] = "Opisthokonta-Metazoa"
    df$Taxa[df$Supergroup == "Stramenopiles"] = "Stramenopiles-Other"
    df$Taxa[df$Class == "Bicoecea"] = "Stramenopiles-Bicoecea"
    df$Taxa[df$Division == "Ochrophyta"] = "Stramenopiles-Ochrophyta"
    mast <- unique(filter(df, grepl("MAST", Class)) %>% select(Class))
    mast_list <- as.character(mast$Class)
    df$Taxa[df$Class %in% mast_list] = "Stramenopiles-MAST"
    df$Taxa[df$Supergroup == "Archaeplastida"] = "Archaeplastida-Other"
    df$Taxa[df$Division == "Chlorophyta"] = "Archaeplastida-Chlorophyta"
    df$Taxa[df$Supergroup == "Excavata"] = "Excavata"
    df$Taxa[df$Supergroup == "Apusozoa"] = "Apusozoa"
    df$Taxa[df$Supergroup == "Amoebozoa"] = "Amoebozoa"
    df$Taxa[df$Supergroup == "Rhizaria"] = "Rhizaria-Other"
    df$Taxa[df$Division == "Cercozoa"] = "Rhizaria-Cercozoa"
    df$Taxa[df$Division == "Radiolaria"] = "Rhizaria-Radiolaria"
    return(df)
}

Apply PR2 curation to 18S data.

gr_counts_wtax <- pr2_curate(gr_counts_name)
# head(gr_counts_wtax[1:3,]) unique(gr_counts_wtax$Taxa)

Output is the full ASV table with added columns for curated taxonomy. Above also provides a list of the unique taxonomic names assigned.

gr_counts_wtax_samplesonly <- subset(gr_counts_wtax, !(Sampletype == "control"))

## To average across replicates, modify SUPR sample names
gr_counts_filter <- gr_counts_wtax_samplesonly
gr_counts_filter$SAMPLEID <- sub("SUPRS9", "SUPR", gr_counts_filter$SAMPLEID)
gr_counts_filter$SAMPLEID <- sub("SUPRS11", "SUPR", gr_counts_filter$SAMPLEID)
gr_counts_filter$SAMPLEID <- sub("SUPRS10", "SUPR", gr_counts_filter$SAMPLEID)
gr_counts_filter$SAMPLEID <- sub("SUPRS2", "SUPR", gr_counts_filter$SAMPLEID)
gr_counts_filter$SAMPLEID <- sub("SUPRS3", "SUPR", gr_counts_filter$SAMPLEID)
gr_counts_filter$SAMPLEID <- sub("SUPRS1", "SUPR", gr_counts_filter$SAMPLEID)

5.1.1 Report sequence stats after curation

# Sum of all sequences
a <- sum(gr_counts_filter %>% filter(!(SAMPLEID == "CTRL")) %>% select(COUNT))
a
## [1] 1434482
# Total ASVs
dim(unique(gr_counts_filter %>% filter(!(SAMPLEID == "CTRL")) %>% select(Feature.ID)))[1]
## [1] 9028
# Percentage of all sequences Unassigned Eukaryote
x <- sum(gr_counts_filter %>% filter(!(SAMPLEID == "CTRL")) %>% filter(Taxon_updated == 
    "Eukaryota") %>% select(COUNT))
100 * (x/a)
## [1] 2.823876
# Total ASVs left 'Unassigned-Eukaryote'
dim(unique(gr_counts_filter %>% filter(!(SAMPLEID == "CTRL")) %>% filter(Taxon_updated == 
    "Eukaryota") %>% select(Feature.ID)))[1]
## [1] 1058
# Percentage of all sequences assigned Opisthokonts
x <- sum(gr_counts_filter %>% filter(!(SAMPLEID == "CTRL")) %>% filter(Supergroup == 
    "Opisthokonta") %>% select(COUNT))
100 * (x/a)
## [1] 12.92606
dim(unique(gr_counts_filter %>% filter(!(SAMPLEID == "CTRL")) %>% filter(Supergroup == 
    "Opisthokonta") %>% select(Feature.ID)))[1]
## [1] 615

5.2 Prepare dataframe to for bar plot

5.2.1 Average ASV sequence count across replicate samples

Average ASV sequence counts across replicate samples, COUNT_AVG column will now equal the ASV sequence count value across replicates

gr_counts_avg_wtax <- gr_counts_filter %>% mutate(LocationName = case_when(LOCATION_SPECIFIC == 
    "Plume036" ~ "Candelabra Plume", LOCATION_SPECIFIC == "Plume096" ~ "Mt Edwards Plume", 
    TRUE ~ as.character(LocationName))) %>% group_by(Feature.ID, SAMPLEID, Sampletype, 
    LOCATION_SPECIFIC, LocationName, Taxon_updated, Kingdom, Supergroup, Division, 
    Class, Order, Family, Genus, Species, Taxa) %>% summarise(COUNT_AVG = mean(COUNT)) %>% 
    as.data.frame
# dim(gr_counts_filter);dim(gr_counts_avg_wtax) tmp <- gr_counts_avg_wtax %>%
# select(Taxa, Taxon_updated, Kingdom, Supergroup, Division, Class, Order,
# Family, Genus, Species) %>% distinct() %>% data.frame write_delim(tmp, path =
# 'tax-tmp-2.txt', delim = '\t') unique(gr_counts_avg_wtax$Taxa)
# unique(gr_counts_avg_wtax$LocationName)
# Save output and view save(gr_counts_filter,gr_counts_wtax, gr_counts_avg_wtax,
# file='data-input/GordaRidge-ASVtable-avg-19-08-2020.RData')

5.2.2 Sum ASV sequence counts to taxonomic level

# See above load(file='data-input/GordaRidge-ASVtable-avg-19-08-2020.RData',
# verbose = T)

Now sum ASV counts by curated taxonomic level. Below generates both summed sequences from samples averages across replicates and for samples with replicates.

# Sum averaged counts at curated taxa level
gr_counts_avg_TAXA <- gr_counts_avg_wtax %>% # Remove control samples & bsw with too few sequences
filter(!(Sampletype == "Control")) %>% filter(!(LOCATION_SPECIFIC == "BSW020")) %>% 
    # sum by like taxa
group_by(SAMPLEID, Sampletype, LocationName, Taxa) %>% summarise(SUM = sum(COUNT_AVG)) %>% 
    unite(SAMPLE, LocationName, Sampletype, SAMPLEID, sep = " ", remove = FALSE) %>% 
    data.frame
# View(gr_counts_avg_TAXA) head(gr_counts_avg_TAXA)

# head(gr_counts_filter) unique(gr_counts_filter$SAMPLEID)
# unique(gr_counts_filter$LOCATION_SPECIFIC)
# unique(gr_counts_filter$LocationName)

# Sum each replicate separately to curated taxa level
gr_counts_wreps_TAXA <- gr_counts_filter %>% mutate(LocationName = case_when(LOCATION_SPECIFIC == 
    "Plume036" ~ "Candelabra Plume", LOCATION_SPECIFIC == "Plume096" ~ "Mt Edwards Plume", 
    TRUE ~ as.character(LocationName))) %>% # Remove control samples & bsw with too few sequences
filter(!(Sampletype == "Control")) %>% filter(!(LOCATION_SPECIFIC == "BSW020")) %>% 
    # sum by like taxa
group_by(SAMPLEID, Sampletype, LocationName, LOCATION_SPECIFIC, Taxa) %>% summarise(SUM = sum(COUNT)) %>% 
    mutate(locationspecific_mod = case_when(LOCATION_SPECIFIC == "Plume001" ~ "NearVent001", 
        TRUE ~ as.character(LOCATION_SPECIFIC))) %>% unite(SAMPLE, LocationName, 
    Sampletype, SAMPLEID, sep = " ", remove = FALSE) %>% unite(SAMPLE_REPS, LocationName, 
    Sampletype, SAMPLEID, locationspecific_mod, sep = " ", remove = FALSE) %>% data.frame
# unique(gr_counts_wreps_TAXA$SAMPLE_REPS)
sample_order_all <- c("Shallow seawater in situ sterivex", "Deep seawater in situ sterivex", 
    "Near vent BW in situ sterivex", "Near vent BW Grazing T0", "Near vent BW Grazing T24", 
    "Near vent BW Grazing T36", "Mt Edwards Plume in situ sterivex", "Mt Edwards Vent in situ SUPR", 
    "Mt Edwards Vent Grazing T0", "Mt Edwards Vent Grazing T36", "Venti Latte Vent in situ SUPR", 
    "Venti Latte Vent Grazing T0", "Venti Latte Vent Grazing T36", "Candelabra Plume in situ sterivex", 
    "Candelabra Vent in situ SUPR", "Candelabra Vent Grazing T24", "SirVentsAlot Vent in situ SUPR", 
    "SirVentsAlot Vent Grazing T24")

supp_table_seq <- gr_counts_avg_TAXA %>% select(SAMPLE, Taxa, SUM) %>% pivot_wider(names_from = SAMPLE, 
    values_from = SUM, values_fill = 0) %>% arrange(Taxa) %>% select(Taxa, sample_order_all)
# View(supp_table_seq) write_delim(supp_table_seq, path =
# 'Suppl-18s-seq-total.txt', delim = '\t') head(gr_counts_avg_wtax)
supp_table_ASV <- gr_counts_avg_wtax %>% # Remove control samples
filter(!(Sampletype == "Control")) %>% # total ASVs by like taxa
group_by(SAMPLEID, Sampletype, LocationName, Taxa) %>% summarise(ASV_total = n_distinct(Feature.ID)) %>% 
    unite(SAMPLE, LocationName, Sampletype, SAMPLEID, sep = " ", remove = TRUE) %>% 
    pivot_wider(names_from = SAMPLE, values_from = ASV_total, values_fill = 0) %>% 
    arrange(Taxa) %>% select(Taxa, sample_order_all)
# View(supp_table_ASV) write_delim(supp_table_ASV, path =
# 'Suppl-18s-asv-total.txt', delim = '\t')

5.2.3 Plot factoring and parameters

# unique(gr_counts_avg_TAXA$Taxa)
level2ORDER <- c("Alveolata-Ciliates", "Alveolata-Dinoflagellates", "Alveolata-Syndiniales", 
    "Alveolata-Other", "Rhizaria-Cercozoa", "Rhizaria-Radiolaria", "Rhizaria-Other", 
    "Stramenopiles-MAST", "Stramenopiles-Ochrophyta", "Stramenopiles-Bicoecea", "Stramenopiles-Other", 
    "Hacrobia-Cryptophyta", "Hacrobia-Haptophyta", "Hacrobia-Other", "Amoebozoa", 
    "Excavata", "Apusozoa", "Archaeplastida-Chlorophyta", "Archaeplastida-Other", 
    "Opisthokonta-Fungi", "Opisthokonta-Metazoa", "Opisthokonta-Other", "Unassigned-Eukaryote")

level2color <- c("#f1eef6", "#d7b5d8", "#df65b0", "#ce1256", "#fc9272", "#ef3b2c", 
    "#800026", "#fff7bc", "#fec44f", "#d95f0e", "#a63603", "#74c476", "#238b45", 
    "#00441b", "#7fcdbb", "#084081", "#c6dbef", "#2b8cbe", "#016c59", "#bcbddc", 
    "#807dba", "#54278f", "#bdbdbd", "black")
gr_counts_avg_TAXA$LEVEL2ORDER <- factor(gr_counts_avg_TAXA$Taxa, levels = level2ORDER)
names(level2color) <- level2ORDER

sample_order_all <- c("Shallow seawater in situ sterivex", "Deep seawater in situ sterivex", 
    "Near vent BW in situ sterivex", "Near vent BW Grazing T0", "Near vent BW Grazing T24", 
    "Near vent BW Grazing T36", "Mt Edwards Plume in situ sterivex", "Mt Edwards Vent in situ SUPR", 
    "Mt Edwards Vent Grazing T0", "Mt Edwards Vent Grazing T36", "Venti Latte Vent in situ SUPR", 
    "Venti Latte Vent Grazing T0", "Venti Latte Vent Grazing T36", "Candelabra Plume in situ sterivex", 
    "Candelabra Vent in situ SUPR", "Candelabra Vent Grazing T24", "SirVentsAlot Vent in situ SUPR", 
    "SirVentsAlot Vent Grazing T24")

sample_name_all <- c("Shallow BSW", "Deep BSW", "Near vent BW", "Near vent BW T0", 
    "Near vent BW T23", "Near vent BW T35", "Mt Edwards Plume", "Mt Edwards Vent", 
    "Mt Edwards Vent T0", "Mt Edwards Vent  T36", "Venti Latte Vent", "Venti Latte Vent T0", 
    "Venti Latte Vent T29", "Candelabra Plume", "Candelabra Vent", "Candelabra Vent T26", 
    "Sir Ventsalot Vent", "Sir Ventsalot Vent T24")
gr_counts_avg_TAXA$SAMPLE_ORDER <- factor(gr_counts_avg_TAXA$SAMPLE, levels = sample_order_all, 
    labels = sample_name_all)


exporder <- c("sterivex", "SUPR", "T0", "T24", "T36")
gr_counts_avg_TAXA$SAMPLEID_ORDER <- factor(gr_counts_avg_TAXA$SAMPLEID, levels = exporder)
gr_counts_avg_TAXA$LOCATION_ORDER <- factor(gr_counts_avg_TAXA$LocationName, levels = c("Shallow seawater", 
    "Deep seawater", "Near vent BW", "Mt Edwards Plume", "Mt Edwards Vent", "Venti Latte Vent", 
    "Candelabra Plume", "Candelabra Vent", "SirVentsAlot Vent"))
# head(gr_counts_avg_TAXA)

# Factor for dataframe with replicates
gr_counts_wreps_TAXA$LEVEL2ORDER <- factor(gr_counts_wreps_TAXA$Taxa, levels = level2ORDER)
gr_counts_wreps_TAXA$SAMPLE_ORDER <- factor(gr_counts_wreps_TAXA$SAMPLE, levels = sample_order_all, 
    labels = sample_name_all)  # Factor by sample, but will plot x as sample with reps
gr_counts_wreps_TAXA$SAMPLEID_ORDER <- factor(gr_counts_wreps_TAXA$SAMPLEID, levels = exporder)
gr_counts_wreps_TAXA$LOCATION_ORDER <- factor(gr_counts_wreps_TAXA$LocationName, 
    levels = c("Shallow seawater", "Deep seawater", "Near vent BW", "Mt Edwards Plume", 
        "Mt Edwards Vent", "Venti Latte Vent", "Candelabra Plume", "Candelabra Vent", 
        "SirVentsAlot Vent"))

5.3 Protistan community barplots

barplot_lev2 <- function(df) {
    ggplot(df, aes(x = SAMPLE_ORDER, y = SUM, fill = LEVEL2ORDER)) + geom_bar(stat = "identity", 
        position = "fill", color = "black") + scale_fill_manual(values = level2color) + 
        scale_y_continuous(expand = c(0, 0)) + theme(legend.position = "right", panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(), panel.border = element_blank(), panel.background = element_blank(), 
        axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, color = "black", 
            size = 12), axis.text.y = element_text(color = "black", size = 12), axis.title = element_text(color = "black", 
            size = 12), strip.text = element_blank(), legend.title = element_blank()) + 
        labs(x = "", y = "Relative abundance") + facet_grid(. ~ LOCATION_ORDER, space = "free", 
        scales = "free") + guides(fill = guide_legend(ncol = 1))
}
barplot_lev2_wreps <- function(df) {
    ggplot(df, aes(x = SAMPLE_REPS, y = SUM, fill = LEVEL2ORDER)) + geom_bar(stat = "identity", 
        position = "fill", color = "black") + scale_fill_manual(values = level2color) + 
        scale_y_continuous(expand = c(0, 0)) + theme(legend.position = "right", panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(), panel.border = element_blank(), panel.background = element_blank(), 
        axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, color = "black", 
            size = 12), axis.text.y = element_text(color = "black", size = 12), axis.title = element_text(color = "black", 
            size = 12), strip.text = element_blank(), legend.title = element_blank()) + 
        labs(x = "", y = "Relative abundance") + facet_grid(. ~ SAMPLE_ORDER, space = "free", 
        scales = "free") + guides(fill = guide_legend(ncol = 1))
}

insitu <- c("sterivex", "SUPR")
rm <- c("Unassigned", "Opisthokonta-Other", "Opisthokonta-Fungi", "Opisthokonta-Metazoa")
# svg('figs/barplot-taxalevel-wReps.svg', h = 9, w = 13)
barplot_lev2_wreps(gr_counts_wreps_TAXA)

# dev.off()
insitu <- c("sterivex", "SUPR")
rm <- c("Unassigned", "Opisthokonta-Other", "Opisthokonta-Fungi", "Opisthokonta-Metazoa")

nometaz_all <- barplot_lev2(filter(gr_counts_avg_TAXA, !(Taxa %in% rm)))
nometaz_all

withmetaz_all <- barplot_lev2(filter(gr_counts_avg_TAXA))

# svg('figs/SUPPLEMENTARY-GR-tax-barplot-wmetaz.svg', w = 10, h = 8)
withmetaz_all

# dev.off()

6 Ordination analysis

6.1 Import 18S-derived data

load("data-input/GordaRidge-ASVtable-avg-19-08-2020.RData")
ventnames <- read.delim("data-input/ventnames-gordaridge.txt")
library(reshape2)
library(vegan)
library(dplyr)
library(ade4)
library(compositions)
library(tidyverse)
library(purrr)
library(cluster)
library(RColorBrewer)
library(ape)
# ventnames
# Remove controls, pivot wider, and make feature ID rownames
gr_nums_wide <- counts_decont %>% filter(!(SAMPLEID == "CTRL")) %>% filter(!(grepl("GordaRidge_BSW020", 
    SAMPLE))) %>% select(Feature.ID, SAMPLE, COUNT, REP) %>% left_join(ventnames, 
    by = c(SAMPLE = "SAMPLENAME")) %>% mutate(LocationName = case_when(LOCATION_SPECIFIC == 
    "Plume036" ~ "Candelabra Plume", LOCATION_SPECIFIC == "Plume096" ~ "Mt Edwards Plume", 
    TRUE ~ as.character(LocationName))) %>% unite(SAMPLE_MOD, LocationName, Sampletype, 
    SAMPLEID, REP, sep = "-") %>% select(Feature.ID, COUNT, SAMPLE_MOD) %>% pivot_wider(names_from = SAMPLE_MOD, 
    values_from = COUNT, values_fill = 0) %>% column_to_rownames(var = "Feature.ID")

6.2 Cluster analysis

# Fix column names
gr_for_dendro <- gr_nums_wide
colnames(gr_for_dendro) <- gsub(x = names(gr_for_dendro), pattern = "-", replacement = " ")

# Relative abundance
relabun <- decostand(gr_for_dendro, MARGIN = 2, method = "total")
# colSums(relabun) # Should all equal to 1

# Cluster dendrogram (average hierarchical clustering)
cluster_gr <- hclust(dist(t(relabun)), method = "average")
# ?hclust() ?dist() ?decostand
library(ggdendro)
library(dendextend)

dendro <- as.dendrogram(cluster_gr)
gr_dendro <- dendro_data(dendro, type = "rectangle")
gr_dendro_plot <- ggplot(segment(gr_dendro)) + geom_segment(aes(x = x, y = y, xend = xend, 
    yend = yend)) + coord_flip() + scale_y_reverse(expand = c(0.2, 0.5), breaks = c(0, 
    0.2, 0.4, 0.6, 0.8)) + geom_text(aes(x = x, y = y, label = label, angle = 0, 
    hjust = 0), data = label(gr_dendro)) + theme_dendro() + labs(y = "Dissimilarity") + 
    theme(axis.text.x = element_text(color = "black", size = 14), axis.line.x = element_line(color = "#252525"), 
        axis.ticks.x = element_line(), axis.title.x = element_text(color = "black", 
            size = 14))
# svg('figs/SUPPLEMENTARY-dendrogram-wreps.svg', w = 10, h = 8)
gr_dendro_plot

# dev.off()

6.3 Data transformation-CLR

# # Transform data - with CLR # Log-ratio
log_rats <- data.frame(compositions::clr(t(gr_nums_wide)))
# log_rats # look at eigenvalues
pca_lr <- prcomp(log_rats)
variance_lr <- (pca_lr$sdev^2)/sum(pca_lr$sdev^2)
# head(variance_lr)
barplot(variance_lr, main = "Log-Ratio PCA Screeplot", xlab = "PC Axis", ylab = "% Variance", 
    cex.names = 1.5, cex.axis = 1.5, cex.lab = 1.5, cex.main = 1.5)

> Based on this screeplot - 2 axis are OK, as they show 0.079 and 0.077, respectively, of the variance.

6.4 Plot PCA

# Extract PCA points pca_lr$x
pca_lr_frame <- data.frame(pca_lr$x, SAMPLE = rownames(pca_lr$x))

pca_lr_frame_wNames <- pca_lr_frame %>% rownames_to_column(var = "SAMPLENAME") %>% 
    separate(SAMPLENAME, c("LocationName", "Sampletype", "SampleID", "REP"), "-", 
        remove = FALSE) %>% unite(shape_sample, LocationName, Sampletype, sep = " ", 
    remove = FALSE)
# unique(pca_lr_frame_wNames$LocationName)
# Factor for plotting
sample_order_all <- c("Shallow seawater", "Deep seawater", "Near vent BW", "Mt Edwards Plume", 
    "Mt Edwards Vent", "Venti Latte Vent", "Candelabra Plume", "Candelabra Vent", 
    "SirVentsAlot Vent")
sample_label_all <- c("Shallow BSW", "Deep BSW", "Near vent BW", "Mt. Edwards Plume", 
    "Mt. Edwards", "Venti latte", "Candelabra Plume", "Candelabra", "Sir Ventsalot")
sample_color_all <- c("#bfbbb0", "#413f44", "#6f88af", "#61ac86", "#61ac86", "#711518", 
    "#dfa837", "#dfa837", "#ce536b")
names(sample_color_all) <- sample_label_all

shape_order <- c("Candelabra Plume in situ", "Candelabra Vent Grazing", "Candelabra Vent in situ", 
    "Deep seawater in situ", "Mt Edwards Plume in situ", "Mt Edwards Vent Grazing", 
    "Mt Edwards Vent in situ", "Near vent BW Grazing", "Near vent BW in situ", "Shallow seawater in situ", 
    "SirVentsAlot Vent Grazing", "SirVentsAlot Vent in situ", "Venti Latte Vent Grazing", 
    "Venti Latte Vent in situ")
shapes <- c(24, 21, 21, 22, 24, 21, 21, 23, 23, 22, 21, 21, 21, 21)
fill_color <- c("#dfa837", "white", "#dfa837", "#413f44", "#61ac86", "white", "#61ac86", 
    "white", "#6f88af", "#bfbbb0", "white", "#ce536b", "white", "#711518")
color_color <- c("#dfa837", "#dfa837", "#dfa837", "#413f44", "#61ac86", "#61ac86", 
    "#61ac86", "#6f88af", "#6f88af", "#bfbbb0", "#ce536b", "#ce536b", "#711518", 
    "#711518")
pca_lr_frame_wNames$SAMPLE_ORDER <- factor(pca_lr_frame_wNames$LocationName, levels = rev(sample_order_all), 
    labels = rev(sample_label_all))

pca_lr_frame_wNames$SHAPE_ORDER <- factor(pca_lr_frame_wNames$shape_sample, levels = shape_order)
pca_18s <- ggplot(pca_lr_frame_wNames, 
                  aes(x = PC1, y = PC2,
                      fill = SHAPE_ORDER, 
                      color = SHAPE_ORDER,
                      shape = SHAPE_ORDER)) + #Replace label=SAMPLEID.y
  # geom_text_repel(size = 3,
  #                  box.padding = unit(0.5, "lines"))+
  geom_hline(yintercept = 0) + geom_vline(xintercept = 0, color = "#525252") +
  geom_point(size=4, stroke = 1.5, aes(fill=SHAPE_ORDER, color = SHAPE_ORDER, shape = SHAPE_ORDER)) +
  ylab(paste0('PC2 ',round(variance_lr[2]*100,2),'%')) +
  xlab(paste0('PC1 ',round(variance_lr[1]*100,2),'%')) +
  scale_shape_manual(values = shapes) +
  scale_fill_manual(values = fill_color) +
  scale_color_manual(values = color_color) +
  theme_bw() +
  theme(axis.text = element_text(color="black", size=12),
        legend.title = element_blank())
# pca_18s

6.5 Plot 18S barplot with PCA

# svg('figs/panel-barplot-pca-18S-nolabel.svg', w = 18, h = 8)
plot_grid(nometaz_all, pca_18s, nrow = 1, labels = c("a", "b"), rel_widths = c(1, 
    0.85), align = c("hv"), axis = c("tblr"))

# dev.off()

7 Classify 18S ASVs by distribution

To test the hypothesis that protistan species may be enriched at vent sites compared to surrounding seawater, 18S-derived ASVs were characterized by distribution. ## Import data and classify ASVs

load("data-input/GordaRidge-ASVtable-avg-19-08-2020.RData", verbose = T)
## Loading objects:
##   gr_counts_filter
##   gr_counts_wtax
##   gr_counts_avg_wtax
# unique(gr_counts_avg_wtax[, c('Sampletype', 'LocationName')]) #categories to
# consider unique(gr_counts_avg_wtax$LocationName)

7.0.1 Categorize ASVs based on presence

gr_wide <- gr_counts_avg_wtax %>% type.convert(as.is = TRUE) %>% filter(!(Sampletype == 
    "Control")) %>% filter(COUNT_AVG > 0) %>% unite(sample_type, LocationName, Sampletype, 
    sep = "_") %>% select(Feature.ID, sample_type, COUNT_AVG) %>% pivot_wider(names_from = sample_type, 
    values_from = COUNT_AVG, values_fill = 0, values_fn = sum) %>% rowwise() %>% 
    mutate_at(vars(Feature.ID), factor) %>% mutate(total = sum(c_across(where(is.numeric)))) %>% 
    data.frame
# names(gr_wide)
# Import classifications
classifcation_schema <- read.delim("data-input/vent-asv-classification.txt")

# From the purr function
any_cols <- function(gr_wide) reduce(gr_wide, `|`)

gr_classified <- gr_wide %>% mutate(VENT_x = ifelse(any_cols(across(contains("Vent_in.situ"), 
    ~. > 0)), "vent", ""), VENTGRAZE_x = ifelse(any_cols(across(contains("Vent_Grazing"), 
    ~. > 0)), "ventgraze", ""), NEAR_x = ifelse(any_cols(across(contains("Near.vent.BW_in.situ"), 
    ~. > 0)), "near", ""), NEARGRAZE_x = ifelse(any_cols(across(contains("Near.vent.BW_Grazing"), 
    ~. > 0)), "neargraze", ""), PLUME_x = ifelse(any_cols(across(contains("Plume_in.situ"), 
    ~. > 0)), "plume", ""), BACK_x = ifelse(any_cols(across(contains("seawater"), 
    ~. > 0)), "bsw", "")) %>% unite(COMPILED, ends_with("_x"), sep = "", remove = FALSE) %>% 
    left_join(classifcation_schema) %>% mutate(CLASS_COMPLEX = case_when(total == 
    1 ~ "Unique", TRUE ~ as.character(CLASS_COMPLEX)), CLASS_SIMPLE_I = case_when(total == 
    1 ~ "Unique", TRUE ~ as.character(CLASS_SIMPLE_I)), CLASS_SIMPLE_II = case_when(total == 
    1 ~ "Unique", TRUE ~ as.character(CLASS_SIMPLE_II))) %>% mutate(sirvents_graze = case_when((SirVentsAlot.Vent_Grazing > 
    0 & SirVentsAlot.Vent_in.situ > 0) ~ "sirvents"), candelabra_graze = case_when((Candelabra.Vent_Grazing > 
    0 & Candelabra.Vent_in.situ > 0) ~ "candelabra"), edwards_graze = case_when((Mt.Edwards.Vent_Grazing > 
    0 & Mt.Edwards.Vent_in.situ > 0) ~ "edwards"), latte_graze = case_when((Venti.Latte.Vent_Grazing > 
    0 & Venti.Latte.Vent_in.situ > 0) ~ "latte"), near_graze = case_when((Near.vent.BW_Grazing > 
    0 & Near.vent.BW_in.situ > 0) ~ "near")) %>% unite(COMPILED_graze, ends_with("_graze"), 
    sep = "", remove = FALSE) %>% select(Feature.ID, starts_with("CLASS_"), ends_with("_graze")) %>% 
    distinct() %>% data.frame
# head(gr_classified)

7.1 Combine distribution with original ASV table

Print report on total ASV counts that fall into each category.

gr_sorted <- left_join(gr_counts_avg_wtax, gr_classified) %>% filter(!(Sampletype == 
    "Control"))
# head(gr_sorted)

# Stats
total <- sum(gr_sorted$COUNT_AVG)
total  #1.26 million sequences
## [1] 1260878
gr_sorted_summary_simpleI <- gr_sorted %>% group_by(CLASS_SIMPLE_I) %>% summarise(totalasv = n_distinct(Feature.ID), 
    totalseq = sum(COUNT_AVG)) %>% mutate(Perc_seq = 100 * (totalseq/total)) %>% 
    data.frame

gr_sorted_summary_simpleII <- gr_sorted %>% group_by(CLASS_SIMPLE_II) %>% summarise(totalasv = n_distinct(Feature.ID), 
    totalseq = sum(COUNT_AVG)) %>% mutate(Perc_seq = 100 * (totalseq/total)) %>% 
    data.frame

gr_sorted_summary_complex <- gr_sorted %>% group_by(CLASS_COMPLEX) %>% summarise(totalasv = n_distinct(Feature.ID), 
    totalseq = sum(COUNT_AVG)) %>% mutate(Perc_seq = 100 * (totalseq/total)) %>% 
    data.frame
# View(gr_sorted_summary_simpleI) View(gr_sorted_summary_simpleII)
# View(gr_sorted_summary_complex)
# head(gr_sorted) distribution_simple vs detailed
gr_dist <- gr_sorted %>% select(Feature.ID, CLASS_SIMPLE_I, CLASS_SIMPLE_II) %>% 
    distinct() %>% mutate(DIST_simple = case_when(CLASS_SIMPLE_I == "Background" ~ 
    "Other", CLASS_SIMPLE_I == "Unique" ~ "Other", TRUE ~ CLASS_SIMPLE_I)) %>% select(Feature.ID, 
    DIST_simple, DIST_detail = CLASS_SIMPLE_II) %>% data.frame

# Select grazing enriched samples
gr_dist_grazing <- gr_sorted %>% select(Feature.ID, ends_with("_graze")) %>% distinct() %>% 
    filter(!(COMPILED_graze == "NANANANANA")) %>% add_column(Graze_enriched = "Enriched") %>% 
    data.frame
# dim(gr_dist_grazing) table(gr_dist_grazing$COMPILED_graze)

7.1.1 Include distribution with taxonomic annotations

gr_stats_wtax <- left_join(gr_counts_avg_wtax, gr_dist) %>% filter(!(Sampletype == 
    "Control")) %>% data.frame

gr_wtax_dist_simple <- gr_stats_wtax %>% group_by(Taxa, DIST_simple) %>% summarise(totalasv = n(), 
    totalseq = sum(COUNT_AVG)) %>% ungroup() %>% group_by(Taxa, DIST_simple) %>% 
    summarise(totalasvs = sum(totalasv), sumseqs = sum(totalseq)) %>% mutate(percentseq = sumseqs/sum(sumseqs) * 
    100) %>% pivot_wider(names_from = DIST_simple, names_glue = "{DIST_simple}_{.value}", 
    values_from = c(totalasvs, sumseqs, percentseq)) %>% data.frame
# View(gr_wtax_dist_simple) write_delim(gr_wtax_dist_simple, path =
# 'Distribution-ASVs-bytax.txt', delim = '\t')

gr_wtax_dist_detailed <- gr_stats_wtax %>% group_by(Taxa, DIST_detail) %>% summarise(totalasv = n(), 
    totalseq = sum(COUNT_AVG)) %>% ungroup() %>% group_by(Taxa, DIST_detail) %>% 
    summarise(totalasvs = sum(totalasv), sumseqs = sum(totalseq)) %>% mutate(percentseq = sumseqs/sum(sumseqs) * 
    100) %>% pivot_wider(names_from = DIST_detail, names_glue = "{DIST_detail}_{.value}", 
    values_from = c(totalasvs, sumseqs, percentseq)) %>% data.frame
# View(gr_wtax_dist_detailed) write_delim(gr_wtax_dist_detailed, path =
# 'Distribution-ASVs-bytax-detailed.txt', delim = '\t')

7.2 Plot ASV & sequence abundance by distribution

Generate bar plot that summarized sequence and ASV abundance by distribution of ASV. Simple distribution determined above mentioned in text of manuscript, more detailed outline of ASV classifications for the supplementary section.

gr_stats_wtax_toplot <- gr_stats_wtax %>% unite(sample, LocationName, Sampletype, 
    SAMPLEID, sep = " ", remove = FALSE) %>% group_by(sample, LocationName, Sampletype, 
    SAMPLEID, DIST_detail) %>% summarise(totalasvs = n_distinct(Feature.ID), sumseqs = sum(COUNT_AVG)) %>% 
    data.frame
## `summarise()` regrouping output by 'sample', 'LocationName', 'Sampletype', 'SAMPLEID' (override with `.groups` argument)
# head(gr_stats_wtax_toplot) unique(gr_stats_wtax_toplot$sample)
sample_order_all <- c("Shallow seawater in situ sterivex", "Deep seawater in situ sterivex", 
    "Near vent BW in situ sterivex", "Near vent BW Grazing T0", "Near vent BW Grazing T24", 
    "Near vent BW Grazing T36", "Mt Edwards Plume in situ sterivex", "Mt Edwards Vent in situ SUPR", 
    "Mt Edwards Vent Grazing T0", "Mt Edwards Vent Grazing T36", "Venti Latte Vent in situ SUPR", 
    "Venti Latte Vent Grazing T0", "Venti Latte Vent Grazing T36", "Candelabra Plume in situ sterivex", 
    "Candelabra Vent in situ SUPR", "Candelabra Vent Grazing T24", "SirVentsAlot Vent in situ SUPR", 
    "SirVentsAlot Vent Grazing T24")

sample_name_all <- c("Shallow BSW", "Deep BSW", "Near vent BW", "Near vent BW T0", 
    "Near vent BW T23", "Near vent BW T35", "Mt Edwards Plume", "Mt Edwards Vent", 
    "Mt Edwards Vent T0", "Mt Edwards Vent  T36", "Venti Latte Vent", "Venti Latte Vent T0", 
    "Venti Latte Vent T29", "Candelabra Plume", "Candelabra Vent", "Candelabra Vent T26", 
    "Sir Ventsalot Vent", "Sir Ventsalot Vent T24")

location_order <- c("Shallow seawater", "Deep seawater", "Near vent BW", "Mt Edwards Plume", 
    "Mt Edwards Vent", "Venti Latte Vent", "Candelabra Plume", "Candelabra Vent", 
    "SirVentsAlot Vent")

location_order_name <- c("Shallow BSW", "Deep BSW", "Near vent BW", "Mt. Edwards Plume", 
    "Mt. Edwards", "Venti latte", "Candelabra Plume", "Candelabra", "Sir Ventsalot")


gr_stats_wtax_toplot$SAMPLE_ORDER <- factor(gr_stats_wtax_toplot$sample, levels = sample_order_all, 
    labels = sample_name_all)
exporder <- c("sterivex", "SUPR", "T0", "T24", "T36")
gr_stats_wtax_toplot$SAMPLEID_ORDER <- factor(gr_stats_wtax_toplot$SAMPLEID, levels = exporder)

location_order <- c("Shallow seawater", "Deep seawater", "Near vent BW", "Mt Edwards Plume", 
    "Mt Edwards Vent", "Venti Latte Vent", "Candelabra Plume", "Candelabra Vent", 
    "SirVentsAlot Vent")
location_order_name <- c("Shallow BSW", "Deep BSW", "Near vent BW", "Mt. Edwards Plume", 
    "Mt. Edwards", "Venti latte", "Candelabra Plume", "Candelabra", "Sir Ventsalot")

gr_stats_wtax_toplot$LOCATION_ORDER <- factor(gr_stats_wtax_toplot$LocationName, 
    levels = location_order, labels = location_order_name)
# Factor for distribution
category_order <- c("Vent local", "Vent local (no background, no vent)", "Vent resident", 
    "Vent resident and background", "Background and vent local (w vent)", "Background and vent local (no vent)", 
    "Background", "Other", "Unique")
category_color <- c("#e31a1c", "#fc4e2a", "#feb24c", "#ffeda0", "#c7e9b4", "#7fcdbb", 
    "#1d91c0", "#225ea8", "#0c2c84")
gr_stats_wtax_toplot$CATEGORY_ORDER <- factor(gr_stats_wtax_toplot$DIST_detail, levels = category_order)
names(category_color) <- category_order
totalseq <- ggplot(gr_stats_wtax_toplot, aes(x = SAMPLE_ORDER, y = sumseqs, fill = CATEGORY_ORDER)) + 
    geom_bar(stat = "identity", color = "black", position = "fill") + # scale_fill_brewer(palette = 'Accent') +
scale_fill_manual(values = category_color) + scale_y_continuous(expand = c(0, 0)) + 
    facet_grid(. ~ LOCATION_ORDER, space = "free", scales = "free") + theme(legend.position = "right", 
    panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.border = element_blank(), 
    panel.background = element_blank(), axis.text.x = element_text(angle = 90, hjust = 1, 
        vjust = 0.5, color = "black", face = "bold"), axis.text.y = element_text(color = "black", 
        face = "bold"), strip.text = element_blank(), strip.background = element_blank(), 
    legend.title = element_blank()) + labs(x = "", y = "")
# totalseq
totalasv <- ggplot(gr_stats_wtax_toplot, aes(x = SAMPLE_ORDER, y = totalasvs, fill = CATEGORY_ORDER)) + 
    geom_bar(stat = "identity", color = "black", position = "fill") + scale_fill_manual(values = category_color) + 
    scale_y_continuous(expand = c(0, 0)) + facet_grid(. ~ LOCATION_ORDER, space = "free", 
    scales = "free") + theme(legend.position = "right", panel.grid.major = element_blank(), 
    panel.grid.minor = element_blank(), panel.border = element_blank(), panel.background = element_blank(), 
    axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, color = "black", 
        face = "bold"), axis.text.y = element_text(color = "black", face = "bold"), 
    strip.text = element_blank(), strip.background = element_blank(), legend.title = element_blank()) + 
    labs(x = "", y = "")
# totalasv
# Supplementary figure svg('figs/SUPPLEMENTARY-seq-asv-distribution.svg', h = 8,
# w = 16)
plot_grid(totalseq + labs(y = "Relative sequence abundance") + theme(legend.position = "none"), 
    totalasv + labs(y = "Relative ASV abundance"), totalseq + geom_bar(stat = "identity", 
        color = "black", position = "stack") + labs(y = "Total sequences") + theme(legend.position = "none"), 
    totalasv + geom_bar(stat = "identity", color = "black", position = "stack") + 
        labs(y = "Total ASVs"), labels = c("a", "b", "c", "d"), axis = c("lrtb"), 
    align = c("vh"))

# dev.off()
# save(gr_stats_wtax_toplot, gr_stats_wtax, gr_dist_grazing, gr_dist, file =
# 'data-input/GR-countinfo-withASVdistribution.RData')

# gr_dist = all ASV classification gr_dist_grazing = ASVs that are found in situ
# and in grazing gr_stats_wtax = complete full table with ASV classifications
# gr_stats_wtax_tplot = summarized for making figures

8 18S bar plots - taxonomic resolution

load("data-input/GordaRidge-ASVtable-avg-19-08-2020.RData", verbose = T)
## Loading objects:
##   gr_counts_filter
##   gr_counts_wtax
##   gr_counts_avg_wtax
load("data-input/GR-countinfo-withASVdistribution.RData", verbose = T)
## Loading objects:
##   gr_stats_wtax_toplot
##   gr_stats_wtax
##   gr_dist_grazing
##   gr_dist

8.1 Re-curate taxonomic levels (higher resolution)

# head(gr_stats_wtax)
unique(gr_stats_wtax$Taxa)
##  [1] "Rhizaria-Radiolaria"        "Stramenopiles-MAST"        
##  [3] "Opisthokonta-Metazoa"       "Alveolata-Ciliates"        
##  [5] "Alveolata-Syndiniales"      "Stramenopiles-Ochrophyta"  
##  [7] "Stramenopiles-Other"        "Alveolata-Dinoflagellates" 
##  [9] "Unassigned-Eukaryote"       "Rhizaria-Cercozoa"         
## [11] "Opisthokonta-Other"         "Hacrobia-Haptophyta"       
## [13] "Alveolata-Other"            "Stramenopiles-Bicoecea"    
## [15] "Archaeplastida-Chlorophyta" "Hacrobia-Other"            
## [17] "Archaeplastida-Other"       "Hacrobia-Cryptophyta"      
## [19] "Excavata"                   "Opisthokonta-Fungi"        
## [21] "Amoebozoa"                  "Apusozoa"                  
## [23] "Rhizaria-Other"
# View(gr_stats_wtax %>% filter(Taxa == 'Stramenopiles-Bicoecea'))

This is a another function for further refinement of the taxonomic assignments.

# Add Taxa2 level
expand_taxa2 <- function(df){
    sumseq <- sum(df$COUNT_AVG)
    # Sum asv totals
    df_tmp <- df %>% group_by(Feature.ID) %>% 
      summarise(SUM_ASV = sum(COUNT_AVG)) %>% 
      mutate(RelAbun = 100*(SUM_ASV/sumseq)) %>% 
      filter(RelAbun > 0.01) %>% 
      data.frame
    topASVs18s <- as.character(unique(df_tmp$Feature.ID)) #Select ASVs > 0.01% of data
    df$Taxa <- as.character(df$Taxa); df$Order <- as.character(df$Order)
    df$Class <- as.character(df$Class); df$Division <- as.character(df$Division)
    non_ciliate <- c("Alveolata-Syndiniales", "Alveolata-Dinoflagellates", "Alveolata-Other")
    order <- c("Alveolata-Syndiniales", "Alveolata-Dinoflagellates")
    class <- c("Alveolata-Ciliates", "Opisthokonta-Metazoa", "Opisthokonta-Fungi", "Opisthokonta-Other", "Rhizaria-Cercozoa", "Rhizaria-Radiolaria")
    df2 <- df %>% 
      type.convert(as.is = TRUE) %>%
      mutate(Taxa2 = Taxa) %>% # Duplicate Taxa column
      mutate(Taxa2 = ifelse(Taxa %in% order, Order, Taxa2),
             Taxa2 = ifelse(Taxa %in% class, Class, Taxa2),
             Taxa2 = ifelse(Taxa %in% "Amoebozoa", Division, Taxa2),#
             Taxa2 = ifelse(Taxa %in% "Apusozoa", Division, Taxa2),#
             # Curate Ciliates
             Taxa2 = ifelse(Class == "Spirotrichea", paste(Class, Order, sep = "-"), Taxa2),
             Taxa2 = ifelse(grepl("Spirotrichea_", Taxa2), "Spirotrichea-Other", Taxa2),
             Taxa2 = ifelse(grepl("Spirotrichea-NA", Taxa2), "Spirotrichea-Other", Taxa2),
             Taxa2 = ifelse(grepl("Spirotrichea-Strombidiida_", Taxa2), "Spirotrichea-Strombidiida", Taxa2),
             Taxa2 = ifelse(grepl("Prostomatea_1", Taxa2), "Prostomatea", Taxa2),
             Taxa2 = ifelse(grepl("CONTH_", Taxa2), "Alveolata-Ciliates", Taxa2),
             Taxa2 = ifelse(grepl("CONThreeP", Taxa2), "Alveolata-Ciliates", Taxa2),
             # Curate dinoflagellates and Syndiniales
             Taxa2 = ifelse(grepl("Dino-Group-", Taxa2), "Syndiniales Dino-Groups (I-V)", Taxa2),
             Taxa2 = ifelse(Taxa2 %in% non_ciliate, "Alveolata-Other", Taxa2),
             Taxa2 = ifelse(Division == "Apicomplexa", "Apicomplexa", Taxa2),
             # Curate Radiolaria
             Taxa2 = ifelse(Class == "Acantharea", "Rhizaria-Acantharea", Taxa2),
             # Taxa2 = ifelse(grepl("Acantharea-Group-", Taxa2), "Acantharea-Groups (I,II,VI)", Taxa2),
             Taxa2 = ifelse(Class == "Polycystinea", paste(Class, Order, sep = "-"), Taxa2),
             Taxa2 = ifelse(Taxa2 == "Rhizaria-Radiolaria-Other", "Rhizaria-Radiolaria", Taxa2),
             Taxa2 = ifelse(Taxa2 == "Rhizaria-Cercozoa-Other", "Rhizaria-Cercozoa", Taxa2),
             Taxa2 = ifelse(Taxa2 == "Endomyxa-Ascetosporea", "Endomyxa", Taxa2),
             Taxa2 = ifelse(Taxa2 == "Novel-clade-10-12", "Rhizaria-Cercozoa", Taxa2),
             Taxa2 = ifelse(Taxa2 == "Chlorarachniophyceae", "Rhizaria-Cercozoa", Taxa2),
             Taxa2 = ifelse(Taxa2 == "Rhizaria-Other", "Rhizaria-Other", Taxa2),
             # Add hacrobia resolution
             Taxa2 = ifelse(Taxa2 == "Hacrobia-Other", Division, Taxa2),
             # Add Excavata resolution
             Taxa2 = ifelse(Taxa2 == "Excavata", Division, Taxa2),
             # Curate Stramenopiles
             Taxa2 = ifelse(Taxa2 == "Stramenopiles-Ochrophyta", Class, Taxa2),
             Taxa2 = ifelse(Taxa2 == "Stramenopiles-MAST", "MAST", Taxa2),
             Taxa2 = ifelse(grepl("MOCH-", Taxa2), "MOCH", Taxa2),
             Taxa2 = ifelse(Taxa2 == "Stramenopiles-Bicoecea", Family, Taxa2),
             # Archaeplastidia
             Taxa2 = ifelse(Division == "Streptophyta", "Archaeplastida-Streptophyta", Taxa2),
             # Curate other unknown - Move low abundance ASVs to "Other"
             Taxa2 = ifelse(grepl("_X", Taxa2), Taxa, Taxa2),
             Taxa2 = ifelse(is.na(Taxa2), Taxa, Taxa2),
             Taxa2 = ifelse(Taxa2 == "Stramenopiles-Ochrophyta", "Stramenopiles-Other", Taxa2),
             Taxa2 = ifelse(Taxa2 == "Unassigned-Eukaryote-Other", "Unassigned-Eukaryote", Taxa2),
             # Fixing the designation of "Other"
             Taxa2 = ifelse(Taxa2 == "Alveolata-Syndiniales", "Alveolata-Other", Taxa2),
             Taxa2 = ifelse(Taxa2 == "Alveolata-Dinoflagellates", "Alveolata-Other", Taxa2),
             Taxa2 = ifelse(Taxa2 == "Alveolata-Dinoflagellates", "Alveolata-Other", Taxa2),
             Taxa2 = ifelse(Taxa2 == "Alveolata-Ciliates", "Ciliates-Other", Taxa2),
             Taxa2 = ifelse(Taxa2 == "Alveolata-Ciliates", "Ciliates-Other", Taxa2)
             ) %>%
      mutate(Broad_Taxa = Taxa) %>%
      mutate(Broad_Taxa = ifelse(Broad_Taxa %in% non_ciliate, "Alveolata", Broad_Taxa),
             Broad_Taxa = ifelse(grepl("Rhizaria", Broad_Taxa), "Rhizaria", Broad_Taxa),
             Broad_Taxa = ifelse(grepl("Stramenopiles", Broad_Taxa), "Stramenopiles", Broad_Taxa),
             Broad_Taxa = ifelse(grepl("Archaeplastida", Broad_Taxa), "Archaeplastida", Broad_Taxa),
             Broad_Taxa = ifelse(grepl("Hacrobia", Broad_Taxa), "Hacrobia", Broad_Taxa),
             Broad_Taxa = ifelse(grepl("Opisthokonta", Broad_Taxa), "Opisthokonta", Broad_Taxa)) %>%
  data.frame
  return(df2)
    }
# Apply to ASV table
gr_counts_avg_wtax2 <- expand_taxa2(gr_stats_wtax)
# View(gr_counts_avg_wtax2)

8.2 Include ASV distribution

# Add categories & set up for plotting function
gr_tax_res <- gr_counts_avg_wtax2 %>% left_join(gr_dist) %>% unite(sample, LocationName, 
    Sampletype, SAMPLEID, sep = " ", remove = FALSE) %>% data.frame
# save(gr_tax_res, file = 'GR-alltax-dist-tax2.RData')
# Make table summarizing taxa stats for each sample.  head(gr_tax_res)
asv_seq_taxa <- gr_tax_res %>% group_by(LocationName, Sampletype, Taxa) %>% summarize(ASV_total = n_distinct(Feature.ID), 
    SEQ_sum = sum(COUNT_AVG)) %>% unite(sample, LocationName, Sampletype, sep = "-") %>% 
    pivot_wider(names_from = sample, values_from = c(ASV_total, SEQ_sum), values_fill = 0) %>% 
    data.frame
# head(asv_seq_taxa) dim(asv_seq_taxa)
asv_seq_taxa2 <- gr_tax_res %>% group_by(LocationName, Sampletype, Taxa, Taxa2) %>% 
    summarize(ASV_total = n_distinct(Feature.ID), SEQ_sum = sum(COUNT_AVG)) %>% unite(sample, 
    LocationName, Sampletype, sep = "-") %>% pivot_wider(names_from = sample, values_from = c(ASV_total, 
    SEQ_sum), values_fill = 0) %>% data.frame
# ?pivot_wider View(asv_seq_taxa_levels) head(asv_seq_taxa2) dim(asv_seq_taxa2)
# write_delim(asv_seq_taxa, path = 'taxa-asv-seq-stats.txt', delim = '\t')
# write_delim(asv_seq_taxa2, path = 'taxa2-asv-seq-stats.txt', delim = '\t')

8.3 Plot function

sample_order_all <- c("Shallow seawater in situ sterivex", "Deep seawater in situ sterivex", 
    "Near vent BW in situ sterivex", "Near vent BW Grazing T0", "Near vent BW Grazing T24", 
    "Near vent BW Grazing T36", "Mt Edwards Plume in situ sterivex", "Mt Edwards Vent in situ SUPR", 
    "Mt Edwards Vent Grazing T0", "Mt Edwards Vent Grazing T36", "Venti Latte Vent in situ SUPR", 
    "Venti Latte Vent Grazing T0", "Venti Latte Vent Grazing T36", "Candelabra Plume in situ sterivex", 
    "Candelabra Vent in situ SUPR", "Candelabra Vent Grazing T24", "SirVentsAlot Vent in situ SUPR", 
    "SirVentsAlot Vent Grazing T24")

sample_name_all <- c("Shallow BSW", "Deep BSW", "Near vent BW", "Near vent BW T0", 
    "Near vent BW T23", "Near vent BW T35", "Mt Edwards Plume", "Mt Edwards Vent", 
    "Mt Edwards Vent T0", "Mt Edwards Vent  T36", "Venti Latte Vent", "Venti Latte Vent T0", 
    "Venti Latte Vent T29", "Candelabra Plume", "Candelabra Vent", "Candelabra Vent T26", 
    "Sir Ventsalot Vent", "Sir Ventsalot Vent T24")

location_order <- c("Shallow seawater", "Deep seawater", "Near vent BW", "Mt Edwards Plume", 
    "Mt Edwards Vent", "Venti Latte Vent", "Candelabra Plume", "Candelabra Vent", 
    "SirVentsAlot Vent")

location_order_name <- c("Shallow BSW", "Deep BSW", "Near vent BW", "Mt. Edwards Plume", 
    "Mt. Edwards", "Venti latte", "Candelabra Plume", "Candelabra", "Sir Ventsalot")

gr_tax_res$SAMPLE_ORDER <- factor(gr_tax_res$sample, levels = sample_order_all, labels = sample_name_all)
exporder <- c("sterivex", "SUPR", "T0", "T24", "T36")
gr_tax_res$SAMPLEID_ORDER <- factor(gr_tax_res$SAMPLEID, levels = exporder)

gr_tax_res$LOCATION_ORDER <- factor(gr_tax_res$LocationName, levels = location_order, 
    labels = location_order_name)
# head(gr_tax_res)
prepdf_tax_dist <- function(df) {
    df2 <- df %>% # filter(category_final %in% category) %>% average across replicates
    group_by(Feature.ID, RES_COSMO = DIST_simple, SAMPLE = sample, SAMPLEID, Sampletype, 
        LocationName, Broad_Taxa, Taxon_updated, Taxa, Taxa2) %>% summarise(COUNT_AVG2 = mean(COUNT_AVG)) %>% 
        ungroup() %>% # sum by like taxa
    group_by(RES_COSMO, SAMPLE, SAMPLEID, Sampletype, LocationName, Broad_Taxa, Taxa, 
        Taxa2) %>% summarise(SUM = sum(COUNT_AVG2)) %>% data.frame
    df2$SAMPLENAME <- factor(df2$SAMPLE, levels = sample_order_all, labels = sample_name_all)
    df2$SAMPLEID_ORDER <- factor(df2$SAMPLEID, levels = c("sterivex", "SUPR", "T0", 
        "T24", "T36"))
    df2$LOCATION_ORDER <- factor(df2$LocationName, levels = location_order)
    return(df2)
}
gr_tax_res_toplot <- prepdf_tax_dist(gr_tax_res)
# tmp <- select(gr_tax_res_toplot, Taxa, Taxa2, Broad_Taxa) %>% distinct()
# write_delim(tmp, path = 'tmp-tax.txt', delim = '\t')
# head(gr_tax_res_toplot[1:2,]) View(gr_tax_res_toplot %>% select(Taxa, Taxa2)
# %>% distinct())

8.4 Extract ASV richness

gr_tax_res_richness <- gr_tax_res %>% # Average across replicates
group_by(Feature.ID, RES_COSMO = DIST_simple, SAMPLE = sample, SAMPLEID, Sampletype, 
    LocationName, Broad_Taxa, Taxon_updated, Taxa2) %>% summarise(COUNT_AVG2 = mean(COUNT_AVG)) %>% 
    ungroup() %>% # Get richness for each taxonomic group
group_by(RES_COSMO, Broad_Taxa, Taxa2) %>% summarise(RICHNESS = n_distinct(Feature.ID)) %>% 
    data.frame
## `summarise()` regrouping output by 'Feature.ID', 'RES_COSMO', 'SAMPLE', 'SAMPLEID', 'Sampletype', 'LocationName', 'Broad_Taxa', 'Taxon_updated' (override with `.groups` argument)
## `summarise()` regrouping output by 'RES_COSMO', 'Broad_Taxa' (override with `.groups` argument)
# Explore 'other categories' to ensure removal is OK!
others <- c("Ciliates-Other", "Spirotrichea-Other", "Alveolata-Other", "Rhizaria-Other", 
    "Stramenopiles-Other", "Amoebozoa", "Apusozoa", "Hacrobia-Other", "Archaeplastida-Other")
# colnames(gr_tax_res) tmp <- gr_tax_res %>% filter(Taxa2 %in% others) %>%
# select(Kingdom:Taxa, Taxa2) %>% distinct() View(tmp) hist(tmp$COUNT_AVG)

8.5 Tile plot with all taxonomic groups

gr_relAbun_toheat <- gr_tax_res_toplot %>% filter(!(Sampletype == "Control")) %>% 
    # filter(Taxa %in% 'Alveolata-Ciliates') %>% filter(!(RES_COSMO == 'Other')) %>%
# Determine relative abundance within samples
group_by(Broad_Taxa, SAMPLENAME) %>% mutate(SUMTOTAL = sum(SUM), RelAbun = 100 * 
    (SUM/SUMTOTAL)) %>% # ungroup() %>% group_by(Taxa, SAMPLENAME) %>% mutate(SUMTOTAL_TAXA = sum(SUM),
# RelAbun_Taxa = 100*(SUM/SUMTOTAL_TAXA)) %>%
data.frame
# Re-factor
tax2_order_all <- c("Colpodea", "Heterotrichea", "Karyorelictea", "Litostomatea", 
    "Nassophorea", "Oligohymenophorea", "Phyllopharyngea", "Plagiopylea", "Prostomatea", 
    "Spirotrichea-Choreotrichida", "Spirotrichea-Euplotia", "Spirotrichea-Hypotrichia", 
    "Spirotrichea-Other", "Spirotrichea-Strombidiida", "Spirotrichea-Tintinnida", 
    "Ciliates-Other", "Gonyaulacales", "Gymnodiniales", "Noctilucales", "Peridiniales", 
    "Prorocentrales", "Suessiales", "Torodiniales", "Apicomplexa", "Syndiniales Dino-Groups (I-V)", 
    "Alveolata-Other", "Amoebozoa", "Breviatea", "Lobosa", "Apusomonadidae", "Apusozoa", 
    "Hilomonadea", "Mantamonadidea", "Discoba", "Metamonada", "Hacrobia-Cryptophyta", 
    "Hacrobia-Haptophyta", "Centroheliozoa", "Katablepharidophyta", "Picozoa", "Telonemia", 
    "Hacrobia-Other", "Archaeplastida-Chlorophyta", "Archaeplastida-Streptophyta", 
    "Archaeplastida-Other", "Ascomycota", "Basidiomycota", "Chytridiomycota", "Microsporidiomycota", 
    "Opisthokonta-Fungi", "Annelida", "Arthropoda", "Cnidaria", "Craniata", "Ctenophora", 
    "Echinodermata", "Gastrotricha", "Mollusca", "Nematoda", "Nemertea", "Opisthokonta-Metazoa", 
    "Platyhelminthes", "Porifera", "Rotifera", "Urochordata", "Choanoflagellatea", 
    "Filasterea", "Ichthyosporea", "Opisthokonta-Other", "Endomyxa", "Filosa", "Filosa-Granofilosea", 
    "Filosa-Imbricatea", "Filosa-Sarcomonadea", "Filosa-Thecofilosea", "Rhizaria-Cercozoa", 
    "Polycystinea-Collodaria", "Polycystinea-Nassellaria", "Polycystinea-Spumellarida", 
    "RAD-A", "RAD-B", "RAD-C", "Rhizaria-Acantharea", "Rhizaria-Radiolaria", "Rhizaria-Other", 
    "MAST", "Bacillariophyta", "Bolidophyceae", "Chrysophyceae", "Dictyochophyceae", 
    "MOCH", "Pelagophyceae", "Synurophyceae", "Caecitellaceae", "Cafeteriaceae", 
    "Stramenopiles-Other", "Unassigned-Eukaryote")
gr_relAbun_toheat$TAXA2_ORDER <- factor(gr_relAbun_toheat$Taxa2, levels = rev(tax2_order_all))

broad_taxa_order <- c("Alveolata-Ciliates", "Alveolata", "Rhizaria", "Stramenopiles", 
    "Amoebozoa", "Apusozoa", "Excavata", "Hacrobia", "Archaeplastida", "Opisthokonta", 
    "Unassigned-Eukaryote")

gr_relAbun_toheat$Broad_ORDER <- factor(gr_relAbun_toheat$Broad_Taxa, levels = broad_taxa_order)

broad_color <- c("#ae017e", "#cb181d", "#d94801", "#fe9929", "#005a32", "#016450", 
    "#084594", "#4a1486", "#238b45", "#252525", "#252525")
# View(unique(gr_relAbun_toheat$Taxa)) unique(gr_relAbun_toheat$Taxa2)
others <- c("Ciliates-Other", "Spirotrichea-Other", "Alveolata-Other", "Rhizaria-Other", 
    "Stramenopiles-Other", "Stramenopiles-Bicoecea", "Amoebozoa", "Apusozoa", "Hacrobia-Other", 
    "Archaeplastida-Other")
# Make geom tile plot
tile_tax <- ggplot(gr_relAbun_toheat, aes(x = SAMPLENAME, alpha = RelAbun, fill = Broad_ORDER, 
    y = TAXA2_ORDER)) + geom_tile(color = "white") + scale_fill_manual(values = broad_color) + 
    theme(legend.position = "right", panel.grid.major = element_blank(), panel.grid.minor = element_blank(), 
        panel.border = element_blank(), panel.background = element_blank(), axis.text.x = element_text(angle = 90, 
            hjust = 1, vjust = 0.5, color = "black", size = 8), axis.text.y = element_text(color = "black", 
            size = 8), strip.background = element_blank(), strip.text.y = element_text(angle = 0, 
            hjust = 0), legend.title = element_blank()) + labs(x = "", y = "") + 
    facet_grid(Broad_ORDER ~ RES_COSMO, space = "free", scales = "free")
# ?geom_tile
rm <- c("Opisthokonta-Fungi", "Opisthokonta-Other", "Opisthokonta-Metazoa")
# tile_tax %+% subset(gr_relAbun_toheat, !(Taxa == 'Unassigned-Eukaryote' |
# RES_COSMO == 'Other' | Taxa %in% rm | Taxa2 %in% others))
broad_taxa_order <- c("Alveolata-Ciliates", "Alveolata", "Rhizaria", "Stramenopiles", 
    "Amoebozoa", "Apusozoa", "Excavata", "Hacrobia", "Archaeplastida", "Opisthokonta", 
    "Unassigned-Eukaryote")

gr_tax_res_richness$Broad_ORDER <- factor(gr_tax_res_richness$Broad_Taxa, levels = broad_taxa_order)
gr_tax_res_richness$TAXA2_ORDER <- factor(gr_tax_res_richness$Taxa2, levels = rev(tax2_order_all))


rm <- c("Opisthokonta-Fungi", "Opisthokonta-Other", "Opisthokonta-Metazoa")

# bubble plot richness
richness_df <- gr_tax_res_richness %>% filter(!(RES_COSMO == "Other")) %>% filter(!(Taxa2 %in% 
    rm | Taxa2 == "Unassigned-Eukaryote")) %>% data.frame
# 
richness_plot <- ggplot(richness_df, aes(x = RES_COSMO, y = TAXA2_ORDER)) + geom_point(aes(size = RICHNESS)) + 
    scale_size_continuous(range = c(0.2, 4)) + facet_grid(Broad_ORDER ~ RES_COSMO, 
    space = "free", scales = "free") + theme(legend.position = "right", panel.grid.major = element_blank(), 
    panel.grid.minor = element_blank(), panel.border = element_blank(), panel.background = element_blank(), 
    axis.text.x = element_text(angle = 90, size = 8), axis.text.y = element_text(size = 8), 
    strip.text = element_blank(), strip.background = element_blank(), legend.title = element_blank()) + 
    labs(x = "", y = "")

# richness_plot
# tile <- get_legend(tile_tax %+% subset(gr_relAbun_toheat, !(Taxa ==
# 'Unassigned-Eukaryote' | RES_COSMO == 'Other' | Taxa %in% rm))) rich <-
# get_legend(richness_plot)

# svg('figs/tile_bubble_plot.svg', w = 18, h = 9)
plot_grid(tile_tax %+% subset(gr_relAbun_toheat, !(Taxa == "Unassigned-Eukaryote" | 
    RES_COSMO == "Other" | Taxa %in% rm | Taxa2 %in% others)), richness_plot %+% 
    subset(richness_df, !(Broad_Taxa == "Opisthokonta" | Broad_Taxa == "Unassigned-Eukaryote" | 
        Taxa2 %in% others)), ncol = 2, axis = c("tb"), align = c("hv"))

# dev.off() ?plot_grid()
# plot_grid(tile_tax %+% subset(gr_relAbun_toheat, !(Taxa ==
# 'Unassigned-Eukaryote' | RES_COSMO == 'Other' | Taxa %in% rm)) +
# geom_tile(color = 'black', fill = 'black'), richness_plot %+%
# subset(richness_df, !(Broad_Taxa == 'Opisthokonta' | Broad_Taxa ==
# 'Unassigned-Eukaryote')), ncol = 2, axis = c('tb'), align = c('hv'))
# Bubble plot
gr_relAbun_tax2_toheat <- gr_tax_res_toplot %>% filter(!(Sampletype == "Control")) %>% 
    # Determine relative abundance by taxa2
group_by(Broad_Taxa, Taxa2) %>% mutate(SUMTOTAL = sum(SUM), RelAbunTaxa2 = 100 * 
    (SUM/SUMTOTAL)) %>% data.frame

# Extract richness at taxa2 level
gr_tax2_res_bysample <- gr_tax_res %>% # Average across replicates
group_by(Feature.ID, RES_COSMO = DIST_simple, SAMPLE = sample, SAMPLEID, Sampletype, 
    LocationName, Broad_Taxa, Taxon_updated, Taxa2) %>% summarise(COUNT_AVG2 = mean(COUNT_AVG)) %>% 
    ungroup() %>% # Get richness for each taxonomic group
group_by(RES_COSMO, SAMPLE, Broad_Taxa, Taxa2) %>% summarise(RICHNESS = n_distinct(Feature.ID)) %>% 
    data.frame
## `summarise()` regrouping output by 'Feature.ID', 'RES_COSMO', 'SAMPLE', 'SAMPLEID', 'Sampletype', 'LocationName', 'Broad_Taxa', 'Taxon_updated' (override with `.groups` argument)
## `summarise()` regrouping output by 'RES_COSMO', 'SAMPLE', 'Broad_Taxa' (override with `.groups` argument)
# Combine relative abundance and richness
gr_tax2_seq_rich <- left_join(gr_relAbun_tax2_toheat, gr_tax2_res_bysample)
## Joining, by = c("RES_COSMO", "SAMPLE", "Broad_Taxa", "Taxa2")
gr_tax2_seq_rich$TAXA2_ORDER <- factor(gr_tax2_seq_rich$Taxa2, levels = rev(tax2_order_all))
gr_tax2_seq_rich$Broad_ORDER <- factor(gr_tax2_seq_rich$Broad_Taxa, levels = broad_taxa_order)
gr_tax2_seq_rich %>% filter(!(Taxa %in% rm)) %>% filter(!RES_COSMO == "Other") %>% 
    filter(!(Taxa == "Unassigned-Eukaryote")) %>% filter(!(Taxa2 %in% others)) %>% 
    ggplot(aes(x = SAMPLENAME, alpha = RelAbunTaxa2, fill = Broad_ORDER, y = TAXA2_ORDER, 
        color = "black")) + geom_point(shape = 21, color = "black", aes(size = RICHNESS, 
    fill = Broad_ORDER, alpha = RelAbunTaxa2)) + scale_size_continuous(range = c(0.5, 
    11)) + scale_fill_manual(values = broad_color) + scale_alpha_continuous(range = c(0.4, 
    1)) + labs(x = "", y = "") + facet_grid(Broad_ORDER ~ RES_COSMO, space = "free", 
    scales = "free") + theme(legend.position = "right", panel.grid.major = element_blank(), 
    panel.grid.minor = element_blank(), panel.border = element_blank(), panel.background = element_blank(), 
    axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, color = "black", 
        size = 8), axis.text.y = element_text(size = 8), strip.text = element_blank(), 
    strip.background = element_blank(), legend.title = element_blank()) + coord_cartesian(clip = "off")

# + guides(fill = guide_legend(override.aes = list(shape = c(21, 21, 2))), shape
# = guide_legend(override.aes = list(color = 'black')))

8.6 Supplementary plots

8.7 Supplementary bar plots

Generate additional supplementary plots as bar plot.

# Factor
ciliate_order <- c("Heterotrichea", "Karyorelictea", "Litostomatea", "Nassophorea", 
    "Oligohymenophorea", "Phyllopharyngea", "Plagiopylea", "Prostomatea", "Spirotrichea-Choreotrichida", 
    "Spirotrichea-Euplotia", "Spirotrichea-Hypotrichia", "Spirotrichea-Other", "Spirotrichea-Strombidiida", 
    "Spirotrichea-Tintinnida", "Ciliates-Other")
gr_tax_res_toplot$CILIATE_ORDER <- factor(gr_tax_res_toplot$Taxa2, levels = ciliate_order)
CILIATE_COLOR <- c("#ffffcc", "#d9f0a3", "#addd8e", "#78c679", "#31a354", "#006837", 
    "#fde0dd", "#fcc5c0", "#fa9fb5", "#f768a1", "#dd3497", "#ae017e", "#7a0177", 
    "#49006a", "#bdbdbd")
names(CILIATE_COLOR) <- ciliate_order
# head(gr_tax_res_toplot)
ciliate_plot <- gr_tax_res_toplot %>% filter(Taxa %in% "Alveolata-Ciliates") %>% 
    filter(!(RES_COSMO == "Other")) %>% ggplot(aes(x = SAMPLENAME, y = SUM, fill = CILIATE_ORDER)) + 
    geom_bar(stat = "identity", position = "fill", color = "black") + scale_fill_manual(values = CILIATE_COLOR) + 
    scale_y_continuous(expand = c(0, 0)) + theme(legend.position = "right", panel.grid.major = element_blank(), 
    panel.grid.minor = element_blank(), panel.border = element_blank(), panel.background = element_blank(), 
    axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, color = "black", 
        face = "bold", size = 12), axis.text.y = element_text(color = "black", face = "bold", 
        size = 12), strip.text.x = element_blank(), strip.background = element_blank(), 
    legend.title = element_blank()) + labs(x = "", y = "Relative abundance") + facet_grid(RES_COSMO ~ 
    LOCATION_ORDER, space = "free", scales = "free")
# ciliate_plot

Repeat high-resolution look at protistan biogeography, but without ciliate groups.

metaz <- c("Opisthokonta-Fungi", "Opisthokonta-Other", "Opisthokonta-Metazoa")
gr_taxa2_else <- gr_tax_res_toplot %>% filter(!(Taxa == "Alveolata-Ciliates")) %>% 
    filter(!(Taxa %in% metaz)) %>% filter(!RES_COSMO == "Other") %>% group_by(SAMPLENAME, 
    Taxa2, RES_COSMO, LOCATION_ORDER) %>% summarise(SUM_2 = sum(SUM)) %>% data.frame
## `summarise()` regrouping output by 'SAMPLENAME', 'Taxa2', 'RES_COSMO' (override with `.groups` argument)
# Other protistan taxa - non-cilaite
tax_order <- c("Alveolata-Other", "Gymnodiniales", "Peridiniales", "Prorocentrales", 
    "Torodiniales", "Noctilucales", "Gonyaulacales", "Suessiales", "Apicomplexa", 
    "Syndiniales Dino-Groups (I-V)", "Breviatea", "Lobosa", "Amoebozoa", "Hilomonadea", 
    "Apusomonadidae", "Mantamonadidea", "Apusozoa", "Archaeplastida-Chlorophyta", 
    "Archaeplastida-Other", "Archaeplastida-Streptophyta", "Metamonada", "Discoba", 
    "Hacrobia-Cryptophyta", "Hacrobia-Haptophyta", "Centroheliozoa", "Katablepharidophyta", 
    "Picozoa", "Telonemia", "Hacrobia-Other", "Endomyxa", "Filosa-Imbricatea", "Filosa-Granofilosea", 
    "Filosa-Thecofilosea", "Filosa-Sarcomonadea", "Filosa", "RAD-A", "RAD-B", "RAD-C", 
    "Polycystinea-Spumellarida", "Polycystinea-Nassellaria", "Polycystinea-Collodaria", 
    "Rhizaria-Acantharea", "Rhizaria-Radiolaria", "Rhizaria-Cercozoa", "Rhizaria-Other", 
    "MAST", "Bacillariophyta", "Bolidophyceae", "Chrysophyceae", "Dictyochophyceae", 
    "MOCH", "Pelagophyceae", "Stramenopiles-Other", "Synurophyceae", "Unassigned-Eukaryote")
color_order <- c("#bdbdbd", "#f7f4f9", "#e7e1ef", "#d4b9da", "#c994c7", "#df65b0", 
    "#e7298a", "#ce1256", "#980043", "#67001f", "#fcae91", "#fb6a4a", "#de2d26", 
    "#fc8d59", "#ef6548", "#d7301f", "#990000", "#ffffcc", "#c2e699", "#78c679", 
    "#238443", "#005a32", "#ece2f0", "#a6bddb", "#67a9cf", "#3690c0", "#02818a", 
    "#016c59", "#014636", "#eff3ff", "#c6dbef", "#9ecae1", "#6baed6", "#3182bd", 
    "#08519c", "#edf8fb", "#ccece6", "#99d8c9", "#66c2a4", "#2ca25f", "#006d2c", 
    "#9ecae1", "#6baed6", "#3182bd", "#08519c", "#fcfbfd", "#efedf5", "#dadaeb", 
    "#bcbddc", "#9e9ac8", "#807dba", "#6a51a3", "#54278f", "#3f007d", "#525252")
gr_taxa2_else$TAXORDER <- factor(gr_taxa2_else$Taxa2, levels = tax_order)
names(color_order) <- tax_order
# tmp <- (gr_tax_res_toplot %>% filter(!(Taxa == 'Alveolata-Ciliates')) %>%
# filter(!(Taxa %in% metaz)) %>% select(Taxa, Taxa2)) View(unique(tmp))

gr_plot_other <- ggplot(gr_taxa2_else, aes(x = SAMPLENAME, y = SUM_2, fill = TAXORDER)) + 
    geom_bar(stat = "identity", position = "fill", color = "black") + scale_fill_manual(values = color_order) + 
    scale_y_continuous(expand = c(0, 0)) + theme(legend.position = "right", panel.grid.major = element_blank(), 
    panel.grid.minor = element_blank(), panel.border = element_blank(), panel.background = element_blank(), 
    axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, color = "black", 
        face = "bold", size = 12), axis.text.y = element_text(color = "black", face = "bold", 
        size = 12), strip.text.x = element_blank(), strip.background = element_blank(), 
    legend.title = element_blank()) + labs(x = "", y = "Relative abundance") + # guides(fill = guide_legend(ncol = 1)) +
facet_grid(RES_COSMO ~ LOCATION_ORDER, space = "free", scales = "free")
# gr_plot_other
# svg('Supplementary-metazoa-plot.svg', h = 8, w = 10) hets_plot %+%
# subset(gr_tax_res_toplot, !(RES_COSMO == 'Other') & (Taxa %in% metaz))
# dev.off()

8.8 ID most abundant ASVs

# What are the most abundant ASVs in each of the taxa2 categories?
# head(gr_tax_res)
gr_topASV_taxa2 <- gr_tax_res %>% select(Feature.ID, RES_COSMO = DIST_simple, Taxa, 
    Taxa2, Taxon_updated, COUNT_AVG) %>% group_by(Feature.ID, RES_COSMO, Taxa, Taxa2, 
    Taxon_updated) %>% summarise(Total = sum(COUNT_AVG)) %>% ungroup() %>% group_by(Taxa, 
    Taxa2) %>% arrange(Taxa2, desc(Total)) %>% top_n(10, Total) %>% data.frame
# gr_topASV_taxa2 write_delim(gr_topASV_taxa2, path =
# 'supptable-topASVs-taxa2.txt', delim = '\t') save(gr_tax_res, file =
# 'GR-18S-ASV-list.RData')

9 Process 16S tag-sequences

Import 16S tag-sequence results (ASV table). Import metadata, modify sample names, and conduct taxonomic curation.

# Import metadata for 16S
ventnames_16 <- read.delim("data-input/ventnames-gordaridge-16S.txt")
# View(ventnames_16)
ventnames_16_mod <- ventnames_16 %>% mutate(location = case_when(grepl("NA108001", 
    SAMPLEID_16S) ~ "NearVent", grepl("NA108036", SAMPLEID_16S) ~ "Plume", grepl("NA108096", 
    SAMPLEID_16S) ~ "Plume", grepl("BSW", SAMPLE_AMY) ~ "BSW", grepl("Vent", LocationName) ~ 
    "Vent"), NA_NUM = SAMPLEID_16S) %>% mutate(NA_NUM = str_replace(NA_NUM, "NA108", 
    "")) %>% mutate(NA_NUM = str_replace(NA_NUM, "NA080", "")) %>% mutate(NA_NUM = str_replace(NA_NUM, 
    "aSTEP", "")) %>% mutate(NA_NUM = str_replace(NA_NUM, "bSTEP", "")) %>% mutate(NA_NUM = str_replace(NA_NUM, 
    "STEP20200226", "")) %>% mutate(NA_NUM = str_replace(NA_NUM, "STEP", "")) %>% 
    unite(NEW_SAMPLEID, location, NA_NUM, sep = "") %>% mutate(LocationName = case_when(grepl("NearVent", 
    NEW_SAMPLEID) ~ "Near vent BW", NEW_SAMPLEID == "Plume036" ~ "Candelabra Plume", 
    NEW_SAMPLEID == "Plume096" ~ "Mt Edwards Plume", grepl("SirVentsAlot", LocationName) ~ 
        "Sir Ventsalot", TRUE ~ as.character(LocationName))) %>% data.frame
# View(ventnames_16_mod)
countbac <- read.delim("data-input/CountTable-wtax-16s-plus3-2020-06-23.txt")

# Remove samples that were repeated
rm <- c("NA108003STEP", "NA108039STEP", "NA108087STEP")

Report stats on 16S tag-sequence data.

tmp <- countbac %>% select(-all_of(rm)) %>% pivot_longer(starts_with("NA"), names_to = "SAMPLEID_16S") %>% 
    left_join(ventnames_16_mod) %>% data.frame
## Joining, by = "SAMPLEID_16S"
sum(tmp$value)  # total sequences
## [1] 1190997
length(unique(tmp$Feature.ID))  # Total ASVs
## [1] 6532

9.1 Format 16S ASV table for plotting

bac_df_plot <- countbac %>%
  separate(Taxon, sep = ";D_[[:digit:]]__", into = c("Domain", "Phylum", "Class", "Order", "Family", "Genus", "Species"), remove = TRUE, extra = "merge") %>% # Warnings are OK with NAs
  mutate_if(is.character, str_replace_all, pattern = "D_0__", replacement = "") %>%
  filter(Domain %in% "Archaea" | Domain %in% "Bacteria") %>% #Select only archaea and bacteria, removing unassigned
  select(-all_of(rm)) %>% # Remove samples we are replacing
  pivot_longer(starts_with("NA"), names_to = "SAMPLEID_16S") %>% 
  left_join(ventnames_16_mod) %>% 
  data.frame
# head(bac_df_plot)
# head(bac_df_plot)
sum(bac_df_plot$value)  # total sequences
## [1] 1190058
length(unique(bac_df_plot$Feature.ID))  # total ASVs
## [1] 6497

9.1.1 Subset 16S ASVs by abundance

bac_df_filtered <- bac_df_plot %>% ungroup() %>% mutate(TOTALSEQ = sum(value)) %>% 
    group_by(Feature.ID) %>% summarise(SUM = sum(value), RELABUN = SUM/TOTALSEQ) %>% 
    filter(RELABUN >= 0.001) %>% add_column(KEEP = "YES") %>% right_join(bac_df_plot) %>% 
    filter(KEEP == "YES") %>% data.frame
## `summarise()` regrouping output by 'Feature.ID' (override with `.groups` argument)
## Joining, by = "Feature.ID"

9.2 Curate 16S taxonomic assignment

Here we are curating the 16S taxonomy assignments so we can get an informative look at the in situ bacteria population diversity and biogeography. Places ASVs below a user designated THRESHOLD into the “Other” category - ASVs that make up < X% of the total data set. For this work, a threshold of 0.1% was used. The other curation of taxonomic assignment was to highlight those groups known to inhabit the region or other chemosynthetic habitats.

9.2.1 Function to modify 16S taxonomy

# Add a column for updated taxonomy name
curate_16s_tax <- function(df, THRESHOLD){
  # List the class and genus level designations that should be named at class level
  class <- c("Alphaproteobacteria", "Deltaproteobacteria", "Gammaproteobacteria", "Nitrososphaeria", "Thermoplasmata")
  genus <- c("Arcobacter","Campylobacter","Hydrogenimonas","Nitratiruptor","Nitratifractor","Sulfurovum","Sulfurimonas","Caminibacter", "Psychrilyobacter", "SUP05 cluster")
  # List the appropriate taxonomic names for this whole level to be placed into "Other" category
  class_other <- c("Verrucomicrobiae")
  phylum_other <- c("Planctomycetes", "Poribacteria", "Cyanobacteria", "WPS-2")
  order_other <- c("Synechococcales")
  totalsumseq <- sum(df$value) # total number of sequences
  tmp_filter <- df %>% 
    group_by(Feature.ID) %>% 
    summarise(SUM = sum(value)) %>% 
    mutate(RELABUN = 100*(SUM/totalsumseq)) %>% 
    filter(RELABUN >= THRESHOLD) %>% #User-defined relabun threshold
    data.frame
  keep_asvs_relabun <- as.character(unique(tmp_filter$Feature.ID))
  df2 <- df %>%
    mutate(Tax_update = Phylum) %>% # Default to filling new taxa level to phylum
    mutate(Tax_update = ifelse(Feature.ID %in% keep_asvs_relabun, Tax_update, "Other"), # Change name to other if it falls below relative abundance Threshold
           Tax_update = ifelse(Class %in% class, paste(Phylum, Class, sep = "-"), Tax_update),
           Tax_update = ifelse(Order == "Methylococcales", paste(Phylum, "Methylococcales", sep = "-"), Tax_update),
           Tax_update = ifelse(Order == "Oceanospirillales", paste(Phylum, "Oceanospirillales", sep = "-"), Tax_update),
           Tax_update = ifelse(Order == "Thioglobaceae", paste(Phylum, "Thioglobaceae", sep = "-"), Tax_update),
           Tax_update = ifelse(Family == "Nitrospinaceae", paste(Phylum, "Nitrospinaceae", sep = "-"), Tax_update),
           Tax_update = ifelse(Class %in% class_other, "Other", Tax_update),
           Tax_update = ifelse(Phylum %in% phylum_other, "Other", Tax_update),
           Tax_update = ifelse(Order %in% order_other, "Other", Tax_update),
           Tax_update = ifelse(Genus %in% genus, paste(Phylum, Genus, sep = "-"), Tax_update))
   return(df2)
}

Removal of known Kitome contamination.

# head(bac_df_plot) # Add updated taxa list to this dataframe
# unique(bac_df_plot$LocationName)
bac_wcuratedtax <- curate_16s_tax(bac_df_plot %>% filter(!(Genus == "Ralstonia")), 
    0.1)  #Will place ASVs <0.1% abundance into 'Other category'
## `summarise()` ungrouping output (override with `.groups` argument)
# unique(bac_wcuratedtax$Tax_update) length(unique(bac_wcuratedtax$Tax_update))
tax_16s <- bac_wcuratedtax %>% select(Tax_update, Domain:Species) %>% distinct()
# write_delim(tax_16s, path = 'tax-key-16s-21-08-2020.txt', delim = '\t')
# Average sequence count for ASVs across replicates (by location name) Save
# output dataframe
bac_gr_avg <- bac_wcuratedtax %>% # Average ASV seq count across replicates
group_by(Feature.ID, LocationName, Tax_update) %>% summarise(AVG_count = mean(value)) %>% 
    data.frame
## `summarise()` regrouping output by 'Feature.ID', 'LocationName' (override with `.groups` argument)
# write_delim(bac_gr_avg, path = 'data-input/16s-gr-data-curated-avg.txt', delim
# = '\t')

9.3 Prepare 16S dataframe for plotting

9.3.1 Average across replicates and sum to taxa

# update exisiting taxonomy
bac_wcuratedtax_toplot <- bac_wcuratedtax %>% # Average ASV seq count across replicates
group_by(Feature.ID, LocationName, Tax_update) %>% summarise(AVG_count = mean(value)) %>% 
    ungroup() %>% group_by(LocationName, Tax_update) %>% summarise(SUM_bytax = sum(AVG_count)) %>% 
    data.frame

# unique(bac_wcuratedtax_toplot$LocationName)
bac_wcuratedtax_toplot$LOCATION <- factor(bac_wcuratedtax_toplot$LocationName, levels = c("Shallow seawater", 
    "Deep seawater", "Near vent BW", "Mt Edwards Plume", "Mt Edwards Vent", "Venti Latte Vent", 
    "Sir Ventsalot", "Candelabra Plume", "Candelabra Vent", "Purple Rain Vent", "Octopus Springs Vent", 
    "Blue Ciliate"))

9.3.2 Generate 16S bar plots

tax_color <- c("#a50026", "#d73027", "#f46d43", "#fdae61", "#fee090", "#ffffbf", 
    "#40004b", "#762a83", "#9970ab", "#c2a5cf", "#e7d4e8", "#d9f0d3", "#a6dba0", 
    "#5aae61", "#1b7837", "#00441b", "#e0f3f8", "#abd9e9", "#74add1", "#4575b4", 
    "#313695", "#8e0152", "#c51b7d", "#de77ae", "#f1b6da", "#fde0ef", "#e6f5d0", 
    "#b8e186", "#7fbc41", "#4d9221", "#276419", "#bababa", "#878787", "#4d4d4d", 
    "#1a1a1a")
tax_order <- c("Epsilonbacteraeota-Arcobacter", "Epsilonbacteraeota-Caminibacter", 
    "Epsilonbacteraeota-Campylobacter", "Epsilonbacteraeota-Hydrogenimonas", "Epsilonbacteraeota-Nitratifractor", 
    "Epsilonbacteraeota-Nitratiruptor", "Epsilonbacteraeota-Sulfurimonas", "Epsilonbacteraeota-Sulfurovum", 
    "Proteobacteria-Alphaproteobacteria", "Proteobacteria-Deltaproteobacteria", "Proteobacteria-Gammaproteobacteria", 
    "Proteobacteria-Methylococcales", "Proteobacteria-Oceanospirillales", "Proteobacteria-SUP05 cluster", 
    "Acidobacteria", "Actinobacteria", "Aquificae", "Bacteroidetes", "Chloroflexi", 
    "Thaumarchaeota-Nitrososphaeria", "Euryarchaeota-Thermoplasmata", "Fusobacteria-Psychrilyobacter", 
    "Marinimicrobia (SAR406 clade)", "Nitrospinae-Nitrospinaceae", "Other")

bac_wcuratedtax_toplot$TAX_ORDER <- factor(bac_wcuratedtax_toplot$Tax_update, levels = tax_order)

barplot_16s <- function(df) {
    ggplot(df, aes(x = LOCATION, y = SUM_bytax, fill = TAX_ORDER)) + geom_bar(stat = "identity", 
        position = "fill", color = "black") + scale_fill_manual(values = tax_color) + 
        scale_y_continuous(expand = c(0, 0)) + theme(legend.position = "right", panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(), panel.border = element_blank(), panel.background = element_blank(), 
        axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, color = "black", 
            face = "bold", size = 12), axis.text.y = element_text(color = "black", 
            face = "bold", size = 12), strip.text = element_blank(), legend.title = element_blank()) + 
        labs(x = "", y = "Relative abundance") + facet_grid(. ~ LOCATION, space = "free", 
        scales = "free") + guides(fill = guide_legend(ncol = 1))
}
rm_loc <- c("Purple Rain Vent", "Octopus Springs Vent", "Blue Ciliate")

# svg('16s-curated-static.svg', h=8, w=8)
barplot_16s(bac_wcuratedtax_toplot %>% filter(!(LOCATION %in% rm_loc)))

# dev.off()
# head(bac_wcuratedtax) Average ASV seq count across replicates tmp <-
# bac_wcuratedtax %>% group_by(Feature.ID, Order, Family, Genus, Species,
# LocationName, Tax_update) %>% summarise(AVG_count = mean(value)) %>%
# filter(!(LocationName %in% rm_loc)) %>% filter(!(LocationName == 'Near vent
# BW')) %>% filter(!(grepl('Plume', LocationName))) %>% data.frame
# unique(bac_wcuratedtax$LocationName)

9.3.3 Data transformation-Jaccard

# head(bac_wcuratedtax)
rm_loc <- c("Purple Rain Vent", "Octopus Springs Vent", "Blue Ciliate")

bac_df_num <- bac_wcuratedtax %>% type.convert(as.is = TRUE) %>% filter(!LocationName %in% 
    rm_loc) %>% unite(SAMPLENAME, LocationName, SAMPLEID, NEW_SAMPLEID, SAMPLEID_16S, 
    sep = "-") %>% select(Feature.ID, SAMPLENAME, value) %>% pivot_wider(names_from = SAMPLENAME, 
    values_from = value, values_fill = 0) %>% column_to_rownames(var = "Feature.ID") %>% 
    as.matrix
# head(bac_df_num)
library(compositions)
df_log_clr <- data.frame(clr(t(bac_df_num)))

# # Ordination - PCA # ?prcomp()
pca_clr <- prcomp(df_log_clr)

# # Check variance
check_variance <- (pca_clr$sdev^2)/sum(pca_clr$sdev^2)
# head(check_variance) # Screeplot, how many axes are appropriate?
barplot(check_variance, main = "Log-Ratio PCA Screeplot", xlab = "PC Axis", ylab = "% Variance", 
    cex.names = 1.5, cex.axis = 1.5, cex.lab = 1.5, cex.main = 1.5)

# Convert to dataframe and parse metadata
df_pca_clr <- data.frame(pca_clr$x, SAMPLENAME = rownames(pca_clr$x))
# head(df_pca_clr)
df_pca_clr_wnames <- df_pca_clr %>% separate(SAMPLENAME, c("LocationName", "Sampletype", 
    "SAMPLEID", "Fastq"), sep = "-") %>% data.frame
# head(df_pca_clr_wnames)
unique(df_pca_clr_wnames$LocationName)
## [1] "Near vent BW"     "Deep seawater"    "Mt Edwards Vent"  "Candelabra Plume"
## [5] "Venti Latte Vent" "Shallow seawater" "Candelabra Vent"  "Mt Edwards Plume"
## [9] "Sir Ventsalot"

9.3.4 Plot 16S PCA

# Factor for plotting sample_order_all_16s <- c('Shallow seawater','Deep
# seawater','Plume','Near vent BW','Mt Edwards Vent','Venti Latte
# Vent','Candelabra Vent','Sir Ventsalot')
sample_order_all_16s <- c("Candelabra Vent", "Mt Edwards Vent", "Sir Ventsalot", 
    "Venti Latte Vent", "Deep seawater", "Shallow seawater", "Near vent BW", "Candelabra Plume", 
    "Mt Edwards Plume")
# sample_color_all
# <-c('#bfbbb0','#413f44','#7d8c55','#6f88af','#61ac86','#711518','#dfa837','#ce536b')
sample_color_all <- c("#dfa837", "#61ac86", "#ce536b", "#711518", "#413f44", "#bfbbb0", 
    "#6f88af", "#dfa837", "#61ac86")
names(sample_color_all) <- sample_order_all_16s
shapes <- c(21, 21, 21, 21, 22, 22, 23, 24, 24)
df_pca_clr_wnames$SAMPLE_ORDER <- factor(df_pca_clr_wnames$LocationName, levels = sample_order_all_16s)
# svg('PCoA-16S-wolabels.svg', h = 8, w = 8)
pca_16s <- ggplot(df_pca_clr_wnames, aes(x = PC1, y = PC2, fill = SAMPLE_ORDER, shape = SAMPLE_ORDER, 
    color = SAMPLE_ORDER)) + geom_point(aes(x = PC1, y = PC2, fill = SAMPLE_ORDER, 
    shape = SAMPLE_ORDER, color = SAMPLE_ORDER), size = 4) + scale_fill_manual(values = sample_color_all) + 
    scale_color_manual(values = sample_color_all) + scale_shape_manual(values = shapes) + 
    ylab(paste0("PC2 ", round(check_variance[2] * 100, 2), "%")) + xlab(paste0("PC1 ", 
    round(check_variance[1] * 100, 2), "%")) + ggtitle("16S - CLR PCA Ordination") + 
    theme_bw() + theme(axis.text = element_text(color = "black", size = 12), legend.title = element_blank()) + 
    geom_hline(yintercept = 0) + geom_vline(xintercept = 0)

# pca_16s
# svg('figs/16s-panel-supplementary.svg', w=16, h=8)
plot_grid(barplot_16s(bac_wcuratedtax_toplot %>% filter(!(LOCATION %in% rm_loc))), 
    pca_16s, axis = c("tblr"), align = c("hv"), labels = c("a", "b"))

# dev.off()

10 ASV selective processes

# load 18S data load('data-input/GR-countinfo-withASVdistribution.RData',
# verbose=T)
# Sort and filter eukaryote ASVs to consider: sumseq <- sum(gr_sorted$COUNT_AVG)
# metaz <- c('Opisthokonta-Fungi', 'Opisthokonta-Other', 'Opisthokonta-Metazoa')
# euk_data_ASV <- gr_sorted %>% filter(Sampletype == 'in situ') %>% #select only
# in situ samples select(Feature.ID, Taxon_updated, COUNT_AVG, LocationName,
# Taxa) %>% group_by(LocationName) %>% # Calculate relative abundance in each
# sample mutate(RelAbun = 100*(COUNT_AVG/sum(COUNT_AVG))) %>% filter(!Taxa ==
# 'Unassigned-Eukaryote') %>% filter(!Taxa %in% metaz) %>% select(Feature.ID,
# LocationName, Taxon_EUK = Taxon_updated, RelAbun, Taxa) %>% mutate(AXIS =
# case_when( grepl('seawater', LocationName) ~ 'Background', TRUE ~ 'Vent_plume'
# )) %>% pivot_wider(id_cols = c(Feature.ID, Taxa), names_from = AXIS,
# values_from = 'RelAbun', values_fn = mean, values_fill = 0) %>%
# filter(!(Vent_plume == 0), !(Background == 0)) %>% mutate(Enriched = case_when(
# Vent_plume > Background ~ 'yes', TRUE ~ 'no' )) %>% data.frame
# length(unique(euk_data_ASV$Feature.ID)) head(euk_data_ASV)
# table(euk_data_ASV$Enriched) View(euk_data_interact)

11 Correlation analysis: 18S-16S

Use these ASVs downstream to explore hypotheses with correlation results. Below set up 16S and 18S rRNA gene output data as phyloseq objects to import into SPIEC-EASI. Following SPIEC-EASI analysis, export as dataframe, add metadata, and process.

11.1 Prepare 16S and 18S data for correlation analysis:

Format input 18S and 16S data, save for correlation analysis.

# load 18S data
load("data-input/GR-countinfo-withASVdistribution.RData", verbose = T)
## Loading objects:
##   gr_stats_wtax_toplot
##   gr_stats_wtax
##   gr_dist_grazing
##   gr_dist
# head(gr_stats_wtax) load 16S data, this has been averaged across replicates
bac_wtax <- read.delim("data-input/16s-gr-data-curated-avg.txt")
# unique(gr_stats_wtax$LocationName) unique(bac_wtax$LocationName)

rm_loc <- c("Purple Rain Vent", "Octopus Springs Vent", "Blue Ciliate")
bac_wtax_mod <- bac_wtax %>% filter(!(LocationName %in% rm_loc)) %>% mutate(LocationName = case_when(LocationName == 
    "Sir Ventsalot" ~ "SirVentsAlot Vent", TRUE ~ as.character(LocationName)))

# unique(gr_stats_wtax$LocationName) unique(bac_wtax_mod$LocationName)
# Sort and filter eukaryote ASVs to consider:
sumseq <- sum(gr_stats_wtax$COUNT_AVG)
metaz <- c("Opisthokonta-Fungi", "Opisthokonta-Other", "Opisthokonta-Metazoa")
head(gr_stats_wtax)
##                         Feature.ID SAMPLEID Sampletype LOCATION_SPECIFIC
## 1 0009645516609bda2246e1955ff9ec1d sterivex    in situ            BSW081
## 2 0030ad8ce44f257c42daf3673bf92197 sterivex    in situ            BSW081
## 3 0030ad8ce44f257c42daf3673bf92197     SUPR    in situ           Vent040
## 4 0030ad8ce44f257c42daf3673bf92197     SUPR    in situ           Vent088
## 5 0030ad8ce44f257c42daf3673bf92197      T24    Grazing           Vent110
## 6 0038478be7fb4f097ce93a5e9341af2a sterivex    in situ            BSW056
##        LocationName
## 1  Shallow seawater
## 2  Shallow seawater
## 3  Venti Latte Vent
## 4   Candelabra Vent
## 5 SirVentsAlot Vent
## 6     Deep seawater
##                                                                                                                          Taxon_updated
## 1 Eukaryota;Rhizaria;Radiolaria;Acantharea;Acantharea-Group-II;Acantharea-Group-II_X;Acantharea-Group-II_XX;Acantharea-Group-II_XX_sp.
## 2                                                  Eukaryota;Stramenopiles;Opalozoa;MAST-3;MAST-3J;MAST-3J_X;MAST-3J_XX;MAST-3J_XX_sp.
## 3                                                  Eukaryota;Stramenopiles;Opalozoa;MAST-3;MAST-3J;MAST-3J_X;MAST-3J_XX;MAST-3J_XX_sp.
## 4                                                  Eukaryota;Stramenopiles;Opalozoa;MAST-3;MAST-3J;MAST-3J_X;MAST-3J_XX;MAST-3J_XX_sp.
## 5                                                  Eukaryota;Stramenopiles;Opalozoa;MAST-3;MAST-3J;MAST-3J_X;MAST-3J_XX;MAST-3J_XX_sp.
## 6                                                Eukaryota;Opisthokonta;Metazoa;Cnidaria;Cnidaria_X;Hydrozoa;Aglaura;Aglaura_hemistoma
##     Kingdom    Supergroup   Division      Class               Order
## 1 Eukaryota      Rhizaria Radiolaria Acantharea Acantharea-Group-II
## 2 Eukaryota Stramenopiles   Opalozoa     MAST-3             MAST-3J
## 3 Eukaryota Stramenopiles   Opalozoa     MAST-3             MAST-3J
## 4 Eukaryota Stramenopiles   Opalozoa     MAST-3             MAST-3J
## 5 Eukaryota Stramenopiles   Opalozoa     MAST-3             MAST-3J
## 6 Eukaryota  Opisthokonta    Metazoa   Cnidaria          Cnidaria_X
##                  Family                  Genus                    Species
## 1 Acantharea-Group-II_X Acantharea-Group-II_XX Acantharea-Group-II_XX_sp.
## 2             MAST-3J_X             MAST-3J_XX             MAST-3J_XX_sp.
## 3             MAST-3J_X             MAST-3J_XX             MAST-3J_XX_sp.
## 4             MAST-3J_X             MAST-3J_XX             MAST-3J_XX_sp.
## 5             MAST-3J_X             MAST-3J_XX             MAST-3J_XX_sp.
## 6              Hydrozoa                Aglaura          Aglaura_hemistoma
##                   Taxa COUNT_AVG  DIST_simple
## 1  Rhizaria-Radiolaria        80        Other
## 2   Stramenopiles-MAST        36 Cosmopolitan
## 3   Stramenopiles-MAST        12 Cosmopolitan
## 4   Stramenopiles-MAST        34 Cosmopolitan
## 5   Stramenopiles-MAST        15 Cosmopolitan
## 6 Opisthokonta-Metazoa        21 Cosmopolitan
##                          DIST_detail
## 1                         Background
## 2       Vent resident and background
## 3       Vent resident and background
## 4       Vent resident and background
## 5       Vent resident and background
## 6 Background and vent local (w vent)
euk_data_interact <- gr_stats_wtax %>%
  type.convert(as.is = TRUE) %>%
  filter(Sampletype == "in situ") %>% #select only in situ samples
  filter(!Taxa %in% metaz) %>% 
  filter(!Taxa == "Unassigned-Eukaryote") %>% 
  select(Feature.ID, Taxon_updated, COUNT_AVG, LocationName) %>% 
  group_by(Feature.ID, Taxon_updated, LocationName) %>% 
  summarise(COUNT_TOTAL = sum(COUNT_AVG)) %>% 
  ungroup() %>% 
  # Calculate relative abundance
  mutate(RelAbun = 100*(COUNT_TOTAL/sumseq)) %>% 
  # Remove ASVs ahead of network analysis
  group_by(Feature.ID) %>% 
  filter(RelAbun > 0.001) %>%
  mutate(sample_appear = n_distinct(LocationName)) %>% #Calculate how many times an ASV appears
  filter(sample_appear > 3) %>% #ASV must appear in at least 3 samples
  filter(COUNT_TOTAL >= 50) %>% #ASV must have at least 10 sequences
  add_column(domain = "euk") %>%
  unite(FEATURE, domain, Feature.ID, sep = "_", remove = TRUE) %>% 
  select(FEATURE, LocationName, Taxon_EUK = Taxon_updated, COUNT = COUNT_TOTAL) %>% 
  data.frame
## `summarise()` regrouping output by 'Feature.ID', 'Taxon_updated' (override with `.groups` argument)
length(unique(gr_stats_wtax$Feature.ID)); length(unique(euk_data_interact$FEATURE))
## [1] 9028
## [1] 328
# View(euk_data_interact)
# head(euk_data_interact)
sumseq <- sum(bac_wtax_mod$AVG_count)
locations_gr <- unique(gr_stats_wtax$LocationName)

bac_data_interact <- bac_wtax_mod %>% 
  filter(LocationName %in% locations_gr) %>% 
  filter(!(Tax_update == "Other")) %>% #Remove "other"
  group_by(Feature.ID, Tax_update, LocationName) %>% 
  summarise(COUNT_TOTAL = sum(AVG_count)) %>% 
  ungroup() %>% 
  add_column(domain = "prok") %>% 
  # Calculate relative abundance
  mutate(RelAbun = 100*(COUNT_TOTAL/sumseq)) %>% 
  # Remove ASVs ahead of network analysis
  group_by(Feature.ID) %>% 
  filter(RelAbun > 0.001) %>%
  mutate(sample_appear = n_distinct(LocationName)) %>% #Calculate how many times an ASV appears
  filter(sample_appear > 3) %>% #ASV must appear in at least 3 samples
  filter(COUNT_TOTAL >= 50) %>% #ASV must have at least 10 sequences
  unite(FEATURE, domain, Feature.ID, sep = "_", remove = TRUE) %>% 
  select(FEATURE, LocationName, Taxon_BAC = Tax_update, COUNT = COUNT_TOTAL) %>% 
  data.frame
## `summarise()` regrouping output by 'Feature.ID', 'Tax_update' (override with `.groups` argument)
length(unique(bac_wtax$Feature.ID)); length(unique(bac_data_interact$FEATURE))
## [1] 3650
## [1] 117
# save(euk_data_interact, bac_data_interact, file =
# 'data-input/Filtered-correlation-R-objects-10-11-2020.RData')

11.1.1 Import as phyloseq objects

euk_df <- euk_data_interact %>% pivot_wider(names_from = LocationName, values_from = COUNT, 
    values_fill = 0) %>% select(order(colnames(.))) %>% data.frame
# head(euk_df)

euk_asv <- as.matrix(select(euk_df, -Taxon_EUK) %>% column_to_rownames(var = "FEATURE"))
euk_tax <- as.matrix(select(euk_df, FEATURE, Taxon_EUK) %>% column_to_rownames(var = "FEATURE"))
# head(bac_asv); head(bac_tax)
row.names(euk_asv) <- row.names(euk_tax)

# Phyloseq import
euk_asv_table <- otu_table(euk_asv, taxa_are_rows = TRUE)
euk_tax_table <- tax_table(euk_tax)
euk_phy <- phyloseq(euk_asv_table, euk_tax_table)
euk_phy
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 328 taxa and 9 samples ]
## tax_table()   Taxonomy Table:    [ 328 taxa by 1 taxonomic ranks ]
bac_df <- bac_data_interact %>% pivot_wider(names_from = LocationName, values_from = COUNT, 
    values_fill = 0) %>% select(order(colnames(.))) %>% data.frame

bac_asv <- as.matrix(select(bac_df, -Taxon_BAC) %>% column_to_rownames(var = "FEATURE"))
bac_tax <- as.matrix(select(bac_df, FEATURE, Taxon_BAC) %>% column_to_rownames(var = "FEATURE"))
# head(bac_asv); head(bac_tax)
row.names(bac_asv) <- row.names(bac_tax)

# Phyloseq import
bac_asv_table <- otu_table(bac_asv, taxa_are_rows = TRUE)
bac_tax_table <- tax_table(bac_tax)
bac_phy <- phyloseq(bac_asv_table, bac_tax_table)
bac_phy
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 117 taxa and 7 samples ]
## tax_table()   Taxonomy Table:    [ 117 taxa by 1 taxonomic ranks ]
# save phyloseq objects to run SpiecEasi in another script
save(bac_phy, euk_phy, file = "data-input/phyloseq-18s-16s-12-11-2020.RData")
# save(bac_phy_tmp, euk_phy_tmp, file = 'phyloseq-18s-16s-TMP.RData') ?spiec.easi

11.2 SpiecEasi

11.2.1 Run SpiecEasi

Run SpiecEasi separately and import output tables to filter significant interactions.

Below command was run on an HPC, while other commands can be run locally. Save output and bring locally below.

# library(SpiecEasi) ?spiec.easi Cross Domain approach se_GR <-
# spiec.easi(list(bac_phy, euk_phy), method = 'mb', nlambda = 40,
# lambda.min.ratio = 1e-2, pulsar.params = list(thresh = 0.05))

## Check output getStability(se_GR) sum(getRefit(se_GR))/2

## Extract weighted matrix se.beta <- as.matrix(symBeta(getOptBeta(se_GR)))
## df_beta <- as.data.frame(se.beta)

## Extract adajency matrix adj.mat <- getRefit(se_GR) df_adj <-
## as.data.frame(as.matrix(adj.mat))

## Assign names from original dataframes colnames(df_beta) <-
## colnames(se_GR$est$data) colnames(df_adj) <- colnames(se_GR$est$data)
## row.names(df_adj) <- colnames(se_GR$est$data) row.names(df_beta) <-
## colnames(se_GR$est$data)

## Save output save(df_adj, df_beta, se_GR, file =
## 'gr-spieceasi-output-20-08-2020.RData')

11.2.2 Process SpiecEasi output

Transform into dataframes to look at relationship of pairs

load("data-input/gr-spieceasi-dfs-12-11-2020.RData", verbose = T)
## Loading objects:
##   df_adj
##   df_beta_weighted
load("data-input/gr-spieceasi-objs-12-11-2020.RData", verbose = T)
## Loading objects:
##   se_GR
##   adj.mat
##   se.beta
# head(df_adj) colnames(df_adj) head(euk_data_interact) head(bac_data_interact)
countbac <- read.delim("data-input/CountTable-wtax-16s-plus3-2020-06-23.txt")
# colnames(countbac)
bac_data_interact_fulltax <- bac_data_interact %>% select(FEATURE, TAX_SHORT = Taxon_BAC) %>% 
    separate(FEATURE, c("domain", "Feature.ID"), sep = "_", remove = FALSE) %>% left_join(select(countbac, 
    Feature.ID, TAX_FULL = Taxon)) %>% select(FEATURE, TAX_FULL, TAX_SHORT) %>% data.frame
## Joining, by = "Feature.ID"
# colnames(gr_stats_wtax) head(gr_tax_res) Make taxonomy key
tax_key_se <- euk_data_interact %>% select(FEATURE, TAX_FULL = Taxon_EUK) %>% separate(FEATURE, 
    c("domain", "Feature.ID"), sep = "_", remove = FALSE) %>% left_join(select(gr_tax_res, 
    Feature.ID, TAX_SHORT = Taxa, EUK_2 = Taxa2, EUK_DIST = DIST_simple)) %>% select(FEATURE, 
    TAX_FULL, TAX_SHORT, EUK_2, EUK_DIST) %>% bind_rows(bac_data_interact_fulltax) %>% 
    distinct() %>% data.frame
## Joining, by = "Feature.ID"
# View(tax_key_se)
reformat_spieceasi <- function(df_in) {
    interaction <- c("PROK-EUK", "EUK-PROK")
    df_in %>% rownames_to_column(var = "SIDEA") %>% pivot_longer(cols = starts_with(c("prok", 
        "euk")), names_to = "SIDEB") %>% mutate(domain_a = case_when(grepl("prok", 
        SIDEA) ~ "PROK", grepl("euk", SIDEA) ~ "EUK"), domain_b = case_when(grepl("prok", 
        SIDEB) ~ "PROK", grepl("euk", SIDEB) ~ "EUK")) %>% mutate(COMBO = paste(domain_a, 
        domain_b, sep = "-")) %>% mutate(COMBO_TYPE = case_when(COMBO %in% interaction ~ 
        "cross", TRUE ~ "same"), SIG_ID = paste(SIDEA, SIDEB, sep = "-")) %>% select(-starts_with("domain")) %>% 
        left_join(select(tax_key_se, TAX_SIDEA = TAX_FULL, everything()), by = c(SIDEA = "FEATURE")) %>% 
        left_join(select(tax_key_se, TAX_SIDEB = TAX_FULL, everything()), by = c(SIDEB = "FEATURE"), 
            suffix = c(".A", ".B")) %>% data.frame
}

df_adj_long <- reformat_spieceasi(df_adj)
df_beta_long <- reformat_spieceasi(df_beta_weighted)

11.2.3 Evaluate statistical parameters determine weight threshold

Evaluate the range of weighted outputs from SpiecEasi. Determine if a threshold can be set.

# Get list of these parameters Adjacency matrix - binary, where 1 = significant
# interaction Boot strapped pvalue, showing weight of each correlation
adj_sig <- df_adj_long %>% filter(value == 1) %>% filter(COMBO_TYPE == "cross") %>% 
    select(everything(), Adjacency = value) %>% left_join(select(df_beta_long, SIG_ID, 
    Weight = value)) %>% data.frame
## Joining, by = "SIG_ID"
# colnames(adj_sig)
dim(adj_sig)
## [1] 1074   15
# head(df_adj_long) table(df_adj_long$value)

dim(adj_sig)  # 1074 significant interactions that are cross-domain
## [1] 1074   15
head(adj_sig)
##                                   SIDEA                                SIDEB
## 1 prok_01dd6ee6ebb76ef5250378057597a969 euk_704617bd30c6df21f779ff5300baf810
## 2 prok_0606870e7caf9d39f42f23dff84c6190 euk_908baaf2bec72eafc520025ef78d0b01
## 3 prok_08932eb86e915caa9c4034ae623d0f45 euk_607390a6a39c3a2bdd7ef41282083418
## 4 prok_08932eb86e915caa9c4034ae623d0f45 euk_75b879fa0e65e7dab54ceb63b5ce5ad3
## 5 prok_08932eb86e915caa9c4034ae623d0f45 euk_a3a866756aa4943b2f4dfbf95badcab0
## 6 prok_08932eb86e915caa9c4034ae623d0f45 euk_ac8ef156389ffd84799bf78d382a0595
##   Adjacency    COMBO COMBO_TYPE
## 1         1 PROK-EUK      cross
## 2         1 PROK-EUK      cross
## 3         1 PROK-EUK      cross
## 4         1 PROK-EUK      cross
## 5         1 PROK-EUK      cross
## 6         1 PROK-EUK      cross
##                                                                       SIG_ID
## 1 prok_01dd6ee6ebb76ef5250378057597a969-euk_704617bd30c6df21f779ff5300baf810
## 2 prok_0606870e7caf9d39f42f23dff84c6190-euk_908baaf2bec72eafc520025ef78d0b01
## 3 prok_08932eb86e915caa9c4034ae623d0f45-euk_607390a6a39c3a2bdd7ef41282083418
## 4 prok_08932eb86e915caa9c4034ae623d0f45-euk_75b879fa0e65e7dab54ceb63b5ce5ad3
## 5 prok_08932eb86e915caa9c4034ae623d0f45-euk_a3a866756aa4943b2f4dfbf95badcab0
## 6 prok_08932eb86e915caa9c4034ae623d0f45-euk_ac8ef156389ffd84799bf78d382a0595
##                                                                                                                TAX_SIDEA
## 1 D_0__Bacteria;D_1__Epsilonbacteraeota;D_2__Campylobacteria;D_3__Campylobacterales;D_4__Arcobacteraceae;D_5__Arcobacter
## 2                                                                                                                   <NA>
## 3  D_0__Bacteria;D_1__Epsilonbacteraeota;D_2__Campylobacteria;D_3__Campylobacterales;D_4__Thiovulaceae;D_5__Sulfurimonas
## 4  D_0__Bacteria;D_1__Epsilonbacteraeota;D_2__Campylobacteria;D_3__Campylobacterales;D_4__Thiovulaceae;D_5__Sulfurimonas
## 5  D_0__Bacteria;D_1__Epsilonbacteraeota;D_2__Campylobacteria;D_3__Campylobacterales;D_4__Thiovulaceae;D_5__Sulfurimonas
## 6  D_0__Bacteria;D_1__Epsilonbacteraeota;D_2__Campylobacteria;D_3__Campylobacterales;D_4__Thiovulaceae;D_5__Sulfurimonas
##                       TAX_SHORT.A EUK_2.A EUK_DIST.A
## 1   Epsilonbacteraeota-Arcobacter    <NA>       <NA>
## 2                            <NA>    <NA>       <NA>
## 3 Epsilonbacteraeota-Sulfurimonas    <NA>       <NA>
## 4 Epsilonbacteraeota-Sulfurimonas    <NA>       <NA>
## 5 Epsilonbacteraeota-Sulfurimonas    <NA>       <NA>
## 6 Epsilonbacteraeota-Sulfurimonas    <NA>       <NA>
##                                                                                                                TAX_SIDEB
## 1    Eukaryota;Hacrobia;Telonemia;Telonemia_X;Telonemia_XX;Telonemia-Group-2;Telonemia-Group-2_X;Telonemia-Group-2_X_sp.
## 2    Eukaryota;Alveolata;Ciliophora;Spirotrichea;Strombidiida_D;Strombidiida_D_X;Strombidiida_D_XX;Strombidiida_D_XX_sp.
## 3                                        Eukaryota;Alveolata;Ciliophora;Oligohymenophorea;Scuticociliatia_1;Philasterida
## 4 Eukaryota;Hacrobia;Haptophyta;Prymnesiophyceae;Prymnesiales;Chrysochromulinaceae;Chrysochromulina;Chrysochromulina_sp.
## 5                                                   Eukaryota;Alveolata;Ciliophora;Litostomatea;Haptoria_6;Lacrymariidae
## 6                                    Eukaryota;Stramenopiles;Opalozoa;MAST-3;MAST-3I;MAST-3I_X;MAST-3I_XX;MAST-3I_XX_sp.
##           TAX_SHORT.B                   EUK_2.B   EUK_DIST.B        Weight
## 1      Hacrobia-Other                 Telonemia Cosmopolitan  1.147447e-06
## 2  Alveolata-Ciliates Spirotrichea-Strombidiida Cosmopolitan -7.159271e-03
## 3  Alveolata-Ciliates         Oligohymenophorea     Resident  1.146349e-02
## 4 Hacrobia-Haptophyta       Hacrobia-Haptophyta Cosmopolitan -2.044291e-01
## 5  Alveolata-Ciliates              Litostomatea     Resident  2.509552e-01
## 6  Stramenopiles-MAST                      MAST     Resident  1.911483e-02
# Isolate the unique interactions and make a table for export
complete_list <- adj_sig %>% filter(COMBO == "EUK-PROK") %>% separate(SIDEA, c("sideA", 
    "ASV_18S"), sep = "_") %>% separate(SIDEB, c("sideB", "ASV_16S"), sep = "_") %>% 
    select(-COMBO, -COMBO_TYPE, -SIG_ID, TAX_18S = TAX_SIDEA, TAX_16S = TAX_SIDEB) %>% 
    data.frame
# head(complete_list) View(complete_list) write_delim(complete_list, path =
# 'Complete-cross-domain-interactions.txt', delim = '\t')

# Write to visualize in cytoscape write.csv(complete_list, 'cross-domain-gr.csv')

11.2.4 Compare relationships at the taxonomic group level

Of the interactions between 18S- and 16S-derived data, we are interested in capturing the putative predator prey relationships

tax_sum_interact <- adj_sig %>% filter(COMBO == "EUK-PROK") %>% separate(SIDEA, c("domain", 
    "ASV_18S"), sep = "_") %>% separate(SIDEB, c("domain2", "ASV_16S"), sep = "_") %>% 
    select(-starts_with("domain"), -COMBO, -COMBO_TYPE, -SIG_ID, -Adjacency) %>% 
    unite(INTERACTION, TAX_SHORT.A, TAX_SHORT.B, sep = "_", remove = FALSE) %>% add_column(COUNT = 1) %>% 
    data.frame
# View(tax_sum_interact)
length(unique(tax_sum_interact$INTERACTION))  #Total significant interactions between euk and bac
## [1] 132
# table(tax_sum_interact$INTERACTION)
# How many 18S ASVs are involved? what taxonomic groups do the interactions
# belong to?  head(tax_sum_interact)
unique(tax_sum_interact$TAX_SHORT.A)
##  [1] "Rhizaria-Radiolaria"        "Hacrobia-Haptophyta"       
##  [3] "Alveolata-Syndiniales"      "Stramenopiles-Other"       
##  [5] "Alveolata-Ciliates"         "Alveolata-Dinoflagellates" 
##  [7] "Stramenopiles-MAST"         "Hacrobia-Other"            
##  [9] "Stramenopiles-Ochrophyta"   "Archaeplastida-Chlorophyta"
## [11] "Rhizaria-Cercozoa"          "Hacrobia-Cryptophyta"
# Table of significant interactions
summary_sig_interactions <- tax_sum_interact %>% select(ASV_18S, ASV_16S, TAX_SHORT.A, 
    COUNT) %>% # distinct() %>%
group_by(TAX_SHORT.A) %>% summarise(UNIQUE_18S_ASVs = n_distinct(ASV_18S), TOTAL_18S_ASVs = sum(COUNT)) %>% 
    data.frame
## `summarise()` ungrouping output (override with `.groups` argument)
# View(summary_sig_interactions) # Included in Table 2

# Classify interactions to taxa level 2
summary_sig_interactions_2 <- tax_sum_interact %>% select(ASV_18S, ASV_16S, TAX_SHORT.A, 
    EUK_2.A, COUNT) %>% # distinct() %>%
group_by(TAX_SHORT.A, EUK_2.A) %>% summarise(UNIQUE_18S_ASVs = n_distinct(ASV_18S), 
    TOTAL_18S_ASVs = sum(COUNT)) %>% data.frame
## `summarise()` regrouping output by 'TAX_SHORT.A' (override with `.groups` argument)
# View(summary_sig_interactions_2)
head(tax_sum_interact)
##                            ASV_18S                          ASV_16S
## 1 01d1a4a17e3a26ee76b34b62cb0cbef8 29b36587344bb929651696c2a41e56cc
## 2 01d1a4a17e3a26ee76b34b62cb0cbef8 9023b3075fc598bad518430ee25519bc
## 3 020295103ca8304135054e04d9110899 2806f0957cc10412ad6a887f25abc970
## 4 020295103ca8304135054e04d9110899 66c28633afa706a1e8785165a4ce933e
## 5 02c7b94c00a919db1d1ef6d9d1ce810c 6e8d876077c3eae3a1f703ac2357d76c
## 6 02c7b94c00a919db1d1ef6d9d1ce810c 929cbf36f791dd363157d90871061cee
##                                                                                       TAX_SIDEA
## 1 Eukaryota;Rhizaria;Radiolaria;RAD-B;RAD-B_X;RAD-B-Group-I;RAD-B-Group-I_X;RAD-B-Group-I_X_sp.
## 2 Eukaryota;Rhizaria;Radiolaria;RAD-B;RAD-B_X;RAD-B-Group-I;RAD-B-Group-I_X;RAD-B-Group-I_X_sp.
## 3 Eukaryota;Rhizaria;Radiolaria;RAD-B;RAD-B_X;RAD-B-Group-I;RAD-B-Group-I_X;RAD-B-Group-I_X_sp.
## 4 Eukaryota;Rhizaria;Radiolaria;RAD-B;RAD-B_X;RAD-B-Group-I;RAD-B-Group-I_X;RAD-B-Group-I_X_sp.
## 5 Eukaryota;Rhizaria;Radiolaria;RAD-B;RAD-B_X;RAD-B-Group-I;RAD-B-Group-I_X;RAD-B-Group-I_X_sp.
## 6 Eukaryota;Rhizaria;Radiolaria;RAD-B;RAD-B_X;RAD-B-Group-I;RAD-B-Group-I_X;RAD-B-Group-I_X_sp.
##                                              INTERACTION         TAX_SHORT.A
## 1                                 Rhizaria-Radiolaria_NA Rhizaria-Radiolaria
## 2    Rhizaria-Radiolaria_Epsilonbacteraeota-Sulfurimonas Rhizaria-Radiolaria
## 3                                 Rhizaria-Radiolaria_NA Rhizaria-Radiolaria
## 4       Rhizaria-Radiolaria_Proteobacteria-SUP05 cluster Rhizaria-Radiolaria
## 5      Rhizaria-Radiolaria_Epsilonbacteraeota-Sulfurovum Rhizaria-Radiolaria
## 6 Rhizaria-Radiolaria_Proteobacteria-Gammaproteobacteria Rhizaria-Radiolaria
##   EUK_2.A EUK_DIST.A
## 1   RAD-B   Resident
## 2   RAD-B   Resident
## 3   RAD-B   Resident
## 4   RAD-B   Resident
## 5   RAD-B   Resident
## 6   RAD-B   Resident
##                                                                                                                                                          TAX_SIDEB
## 1                                                                                                                                                             <NA>
## 2                                            D_0__Bacteria;D_1__Epsilonbacteraeota;D_2__Campylobacteria;D_3__Campylobacterales;D_4__Thiovulaceae;D_5__Sulfurimonas
## 3                                                                                                                                                             <NA>
## 4                                          D_0__Bacteria;D_1__Proteobacteria;D_2__Gammaproteobacteria;D_3__Thiomicrospirales;D_4__Thioglobaceae;D_5__SUP05 cluster
## 5                                             D_0__Bacteria;D_1__Epsilonbacteraeota;D_2__Campylobacteria;D_3__Campylobacterales;D_4__Sulfurovaceae;D_5__Sulfurovum
## 6 D_0__Bacteria;D_1__Proteobacteria;D_2__Gammaproteobacteria;D_3__UBA10353 marine group;D_4__uncultured organism;D_5__uncultured organism;D_6__uncultured organism
##                          TAX_SHORT.B EUK_2.B EUK_DIST.B       Weight COUNT
## 1                               <NA>    <NA>       <NA>  0.004163727     1
## 2    Epsilonbacteraeota-Sulfurimonas    <NA>       <NA> -0.139107326     1
## 3                               <NA>    <NA>       <NA> -0.002829730     1
## 4       Proteobacteria-SUP05 cluster    <NA>       <NA>  0.006002560     1
## 5      Epsilonbacteraeota-Sulfurovum    <NA>       <NA>  0.065705183     1
## 6 Proteobacteria-Gammaproteobacteria    <NA>       <NA> -0.012716197     1
# Classify interactions to taxa level 2
summary_sig_interactions_16s <- tax_sum_interact %>% select(ASV_18S, ASV_16S, TAX_SHORT.B, 
    COUNT) %>% # distinct() %>%
group_by(TAX_SHORT.B) %>% summarise(UNIQUE_16S_ASVs = n_distinct(ASV_16S), TOTAL_16S_ASVs = sum(COUNT)) %>% 
    data.frame
## `summarise()` ungrouping output (override with `.groups` argument)
# View(summary_sig_interactions_16s)


# What is the breakdown of bacteria and archaea ASVs?
head(tax_sum_interact)
##                            ASV_18S                          ASV_16S
## 1 01d1a4a17e3a26ee76b34b62cb0cbef8 29b36587344bb929651696c2a41e56cc
## 2 01d1a4a17e3a26ee76b34b62cb0cbef8 9023b3075fc598bad518430ee25519bc
## 3 020295103ca8304135054e04d9110899 2806f0957cc10412ad6a887f25abc970
## 4 020295103ca8304135054e04d9110899 66c28633afa706a1e8785165a4ce933e
## 5 02c7b94c00a919db1d1ef6d9d1ce810c 6e8d876077c3eae3a1f703ac2357d76c
## 6 02c7b94c00a919db1d1ef6d9d1ce810c 929cbf36f791dd363157d90871061cee
##                                                                                       TAX_SIDEA
## 1 Eukaryota;Rhizaria;Radiolaria;RAD-B;RAD-B_X;RAD-B-Group-I;RAD-B-Group-I_X;RAD-B-Group-I_X_sp.
## 2 Eukaryota;Rhizaria;Radiolaria;RAD-B;RAD-B_X;RAD-B-Group-I;RAD-B-Group-I_X;RAD-B-Group-I_X_sp.
## 3 Eukaryota;Rhizaria;Radiolaria;RAD-B;RAD-B_X;RAD-B-Group-I;RAD-B-Group-I_X;RAD-B-Group-I_X_sp.
## 4 Eukaryota;Rhizaria;Radiolaria;RAD-B;RAD-B_X;RAD-B-Group-I;RAD-B-Group-I_X;RAD-B-Group-I_X_sp.
## 5 Eukaryota;Rhizaria;Radiolaria;RAD-B;RAD-B_X;RAD-B-Group-I;RAD-B-Group-I_X;RAD-B-Group-I_X_sp.
## 6 Eukaryota;Rhizaria;Radiolaria;RAD-B;RAD-B_X;RAD-B-Group-I;RAD-B-Group-I_X;RAD-B-Group-I_X_sp.
##                                              INTERACTION         TAX_SHORT.A
## 1                                 Rhizaria-Radiolaria_NA Rhizaria-Radiolaria
## 2    Rhizaria-Radiolaria_Epsilonbacteraeota-Sulfurimonas Rhizaria-Radiolaria
## 3                                 Rhizaria-Radiolaria_NA Rhizaria-Radiolaria
## 4       Rhizaria-Radiolaria_Proteobacteria-SUP05 cluster Rhizaria-Radiolaria
## 5      Rhizaria-Radiolaria_Epsilonbacteraeota-Sulfurovum Rhizaria-Radiolaria
## 6 Rhizaria-Radiolaria_Proteobacteria-Gammaproteobacteria Rhizaria-Radiolaria
##   EUK_2.A EUK_DIST.A
## 1   RAD-B   Resident
## 2   RAD-B   Resident
## 3   RAD-B   Resident
## 4   RAD-B   Resident
## 5   RAD-B   Resident
## 6   RAD-B   Resident
##                                                                                                                                                          TAX_SIDEB
## 1                                                                                                                                                             <NA>
## 2                                            D_0__Bacteria;D_1__Epsilonbacteraeota;D_2__Campylobacteria;D_3__Campylobacterales;D_4__Thiovulaceae;D_5__Sulfurimonas
## 3                                                                                                                                                             <NA>
## 4                                          D_0__Bacteria;D_1__Proteobacteria;D_2__Gammaproteobacteria;D_3__Thiomicrospirales;D_4__Thioglobaceae;D_5__SUP05 cluster
## 5                                             D_0__Bacteria;D_1__Epsilonbacteraeota;D_2__Campylobacteria;D_3__Campylobacterales;D_4__Sulfurovaceae;D_5__Sulfurovum
## 6 D_0__Bacteria;D_1__Proteobacteria;D_2__Gammaproteobacteria;D_3__UBA10353 marine group;D_4__uncultured organism;D_5__uncultured organism;D_6__uncultured organism
##                          TAX_SHORT.B EUK_2.B EUK_DIST.B       Weight COUNT
## 1                               <NA>    <NA>       <NA>  0.004163727     1
## 2    Epsilonbacteraeota-Sulfurimonas    <NA>       <NA> -0.139107326     1
## 3                               <NA>    <NA>       <NA> -0.002829730     1
## 4       Proteobacteria-SUP05 cluster    <NA>       <NA>  0.006002560     1
## 5      Epsilonbacteraeota-Sulfurovum    <NA>       <NA>  0.065705183     1
## 6 Proteobacteria-Gammaproteobacteria    <NA>       <NA> -0.012716197     1
summary_int <- tax_sum_interact %>% group_by(INTERACTION, EUK_DIST.A) %>% summarise(TOTAL_INTERACTIONS = sum(COUNT)) %>% 
    data.frame
## `summarise()` regrouping output by 'INTERACTION' (override with `.groups` argument)
# View(summary_int %>% filter(EUK_DIST.A != 'Resident'))
# head(tax_sum_interact)
tax_interact_cor <- tax_sum_interact %>% unite(EUK, TAX_SHORT.A, EUK_2.A, sep = "_", 
    remove = TRUE) %>% select(EUK, PROK = TAX_SHORT.B, COUNT) %>% group_by(EUK, PROK) %>% 
    summarise(SUM_COUNT = sum(COUNT)) %>% # pivot_wider(names_from = PROK, values_from = COUNT, values_fn = sum,
# values_fill = 0) %>%
data.frame
## `summarise()` regrouping output by 'EUK' (override with `.groups` argument)
# head(tax_interact_cor)
# ggplot(tax_interact_cor, aes(x = PROK, y = EUK, fill = SUM_COUNT)) +
# geom_tile(stat = 'identity', color = 'black') + scale_fill_gradient(low =
# '#dadaeb', high = '#4a1486') + theme_bw() + theme(axis.text.x =
# element_text(angle = 45, hjust = 1, vjust = 1))

11.2.5 Plot distribution of interactions

library(ggalluvial)
# head(tax_sum_interact)
putative_prey <- tax_sum_interact %>% # filter(!(Broad_Taxa.A == 'Unassigned-Eukaryote')) %>%
group_by(TAX_SHORT.A, TAX_SHORT.B, EUK_DIST.A) %>% summarise(count_sum = sum(COUNT)) %>% 
    data.frame
## `summarise()` regrouping output by 'TAX_SHORT.A', 'TAX_SHORT.B' (override with `.groups` argument)
level2ORDER <- c("Alveolata-Ciliates", "Alveolata-Dinoflagellates", "Alveolata-Syndiniales", 
    "Alveolata-Other", "Rhizaria-Cercozoa", "Rhizaria-Radiolaria", "Rhizaria-Other", 
    "Stramenopiles-MAST", "Stramenopiles-Ochrophyta", "Stramenopiles-Other", "Hacrobia-Cryptophyta", 
    "Hacrobia-Haptophyta", "Hacrobia-Other", "Amoebozoa", "Excavata", "Apusozoa", 
    "Archaeplastida-Chlorophyta", "Archaeplastida-Other", "Opisthokonta-Fungi", "Opisthokonta-Metazoa", 
    "Opisthokonta-Other", "Unassigned-Eukaryote")
level2color <- c("#fa9fb5", "#d7b5d8", "#c994c7", "#ce1256", "#fc9272", "#ef3b2c", 
    "#800026", "#fff7bc", "#fec44f", "#d95f0e", "#74c476", "#238b45", "#00441b", 
    "#7fcdbb", "#084081", "#c6dbef", "#2b8cbe", "#016c59", "#bcbddc", "#807dba", 
    "#54278f", "#bdbdbd")
# level2color <-
# c('#f1eef6','#d7b5d8','#df65b0','#ce1256','#fc9272','#ef3b2c','#800026','#fff7bc','#fec44f','#d95f0e','#74c476','#238b45','#00441b','#7fcdbb','#084081','#c6dbef','#2b8cbe','#016c59','#bcbddc','#807dba','#54278f','#bdbdbd')
putative_prey$LEVEL2ORDER <- factor(putative_prey$TAX_SHORT.A, levels = level2ORDER)
names(level2color) <- level2ORDER
# svg('figs/18s-16s-alluvial-interaction.svg', h = 18, w = 25)
ggplot(putative_prey, aes(axis1 = TAX_SHORT.A, axis2 = TAX_SHORT.B, y = count_sum)) + 
    scale_x_discrete(limits = c("TAX_SHORT.A", "TAX_SHORT.B"), expand = c(0.2, 0.05)) + 
    geom_alluvium(aes(fill = LEVEL2ORDER), alpha = 1, width = 1/3) + scale_fill_manual(values = level2color) + 
    facet_wrap(. ~ EUK_DIST.A, scales = "free") + geom_stratum(size = 0.5, width = 1/3, 
    fill = "#d9d9d9", alpha = 0.7, color = "#525252") + geom_text(stat = "stratum", 
    aes(label = after_stat(stratum)), size = 4, hjust = 1, color = "black") + theme_minimal() + 
    theme(axis.text.x = element_blank(), legend.title = element_blank(), axis.text.y = element_text(color = "black", 
        size = 14), axis.title = element_text(color = "black", size = 14)) + labs(y = "Total Interactions", 
    x = "", title = "18S-16S interactions")

# dev.off()

12 Compare grazing rates and environmental information

gr <- read.delim("Grazing-calc-wCarbon-results.txt")
env <- read.delim("data-input/GR-environ-SAMPLE.txt")
# head(gr) View(unique(gr$SAMPLE))
# Join
gr_env <- gr %>% left_join(env, by = "SAMPLE") %>% select(SAMPLE, SampleOrigin, Vent.name, 
    SAMPLE_ORDER, GrazingRate_hr, Prok_turnover, ugC_L_perday_morono, DEPTH, TEMP, 
    PH, MG, SEA_PER, MICRO) %>% pivot_longer(cols = c(GrazingRate_hr, Prok_turnover, 
    ugC_L_perday_morono), names_to = "Grazing_variable", values_to = "grazing_value") %>% 
    pivot_longer(cols = c(DEPTH, TEMP, PH, MG, SEA_PER, MICRO), names_to = "Env_variable", 
        values_to = "env_value")
# ?pivot_longer head(gr_env) colnames(gr_env)
unique(gr_env$Grazing_variable)
## [1] "GrazingRate_hr"      "Prok_turnover"       "ugC_L_perday_morono"
library(broom)
# View(gr_env)
regression_gr_tmp <- gr_env %>% filter(!(Env_variable == "DEPTH")) %>% filter(!is.na(env_value)) %>% 
    select(SampleOrigin, Vent.name, Grazing_variable, grazing_value, Env_variable, 
        env_value) %>% group_by(Grazing_variable, Env_variable) %>% nest(-Grazing_variable, 
    -Env_variable) %>% mutate(lm_fit = map(data, ~lm(grazing_value ~ env_value, data = .)), 
    tidied = map(lm_fit, tidy)) %>% unnest(tidied) %>% select(Grazing_variable, Env_variable, 
    term, estimate) %>% pivot_wider(names_from = term, values_from = estimate) %>% 
    select(everything(), SLOPE = env_value) %>% data.frame
## Warning: All elements of `...` must be named.
## Did you want `data = c(SampleOrigin, Vent.name, grazing_value, env_value)`?
# head(regression_gr_tmp)

regression_gr_env <- gr_env %>% filter(!(Env_variable == "DEPTH")) %>% filter(!is.na(env_value)) %>% 
    select(SampleOrigin, Vent.name, Grazing_variable, grazing_value, Env_variable, 
        env_value) %>% group_by(Grazing_variable, Env_variable) %>% nest(-Grazing_variable, 
    -Env_variable) %>% mutate(lm_fit = map(data, ~lm(grazing_value ~ env_value, data = .)), 
    glanced = map(lm_fit, glance)) %>% unnest(glanced) %>% select(Grazing_variable, 
    Env_variable, r.squared, adj.r.squared) %>% right_join(regression_gr_tmp) %>% 
    right_join(gr_env) %>% data.frame
## Warning: All elements of `...` must be named.
## Did you want `data = c(SampleOrigin, Vent.name, grazing_value, env_value)`?
## Joining, by = c("Grazing_variable", "Env_variable")
## Joining, by = c("Grazing_variable", "Env_variable")
# View(regression_gr) range(regression_gr$r.squared)
sampleorder <- c("Near vent BW", "Mt. Edwards", "Venti latte", "Candelabra", "Sir Ventsalot")
shapes <- c(23, 21, 21, 21, 21)
samplecolor <- c("#6f88af", "#61ac86", "#711518", "#dfa837", "#ce536b")

regression_gr_env$ENV_LABEL <- factor(regression_gr_env$Env_variable, levels = c("TEMP", 
    "MICRO", "SEA_PER", "PH", "MG"), labels = c(expression("Temperature"^o ~ "C"), 
    bquote("Cells " ~ mL^-1), bquote("Seawater~Percent"), bquote("pH"), bquote("Mg (mM)")))
# X = GrazingRate_hr, Prok_turnover, ugC_L_perday Y = DEPTH, TEMP, PH, MG,
# SEA_PER, MICRO svg('figs/SUPPLEMENTARY-grazing-env-relationship.svg', h = 10, w
# = 10)
plot_grid(regression_gr_env %>% filter(!(Env_variable == "DEPTH")) %>% filter(!(is.na(env_value))) %>% 
    filter(Grazing_variable == "GrazingRate_hr") %>% ggplot(aes(x = env_value, y = grazing_value, 
    fill = SAMPLE_ORDER)) + geom_abline(aes(slope = SLOPE, intercept = X.Intercept.), 
    color = "black", linetype = "dashed", size = 0.5) + geom_point(color = "black", 
    size = 4, aes(shape = SAMPLE_ORDER)) + geom_smooth(method = lm) + scale_fill_manual(values = samplecolor) + 
    scale_shape_manual(values = shapes) + facet_wrap(. ~ ENV_LABEL + round(r.squared, 
    3), scales = "free", ncol = 5, strip.position = "bottom", labeller = label_parsed) + 
    theme_bw() + theme(strip.background = element_blank(), strip.placement = "outside", 
    strip.text = element_text(color = "black", size = 10), axis.title = element_text(color = "black", 
        size = 10), legend.title = element_blank()) + labs(y = bquote("Cells " ~ 
    mL^-1 ~ consumed ~ hr^-1), x = ""), regression_gr_env %>% filter(!(Env_variable == 
    "DEPTH")) %>% filter(!(is.na(env_value))) %>% filter(Grazing_variable == "Prok_turnover") %>% 
    ggplot(aes(x = env_value, y = grazing_value, fill = SAMPLE_ORDER)) + geom_abline(aes(slope = SLOPE, 
    intercept = X.Intercept.), color = "black", linetype = "dashed", size = 0.5) + 
    geom_point(color = "black", size = 4, aes(shape = SAMPLE_ORDER)) + geom_smooth(method = lm) + 
    scale_fill_manual(values = samplecolor) + scale_shape_manual(values = shapes) + 
    facet_wrap(. ~ ENV_LABEL + round(r.squared, 3), scales = "free", ncol = 5, strip.position = "bottom", 
        labeller = label_parsed) + theme_bw() + theme(strip.background = element_blank(), 
    strip.placement = "outside", strip.text = element_text(color = "black", size = 10), 
    axis.title = element_text(color = "black", size = 10), legend.title = element_blank()) + 
    labs(y = bquote("Prokaryote Turnover %" ~ day^-1), x = ""), regression_gr_env %>% 
    filter(!(Env_variable == "DEPTH")) %>% filter(!(is.na(env_value))) %>% filter(Grazing_variable == 
    "ugC_L_perday_morono") %>% ggplot(aes(x = env_value, y = grazing_value, fill = SAMPLE_ORDER)) + 
    geom_abline(aes(slope = SLOPE, intercept = X.Intercept.), color = "black", linetype = "dashed", 
        size = 0.5) + geom_point(color = "black", size = 4, aes(shape = SAMPLE_ORDER)) + 
    geom_smooth(method = lm) + scale_fill_manual(values = samplecolor) + scale_shape_manual(values = shapes) + 
    facet_wrap(. ~ ENV_LABEL + round(r.squared, 3), scales = "free", ncol = 5, strip.position = "bottom", 
        labeller = label_parsed) + theme_bw() + theme(strip.background = element_blank(), 
    strip.placement = "outside", strip.text = element_text(color = "black", size = 10), 
    axis.title = element_text(color = "black", size = 10), legend.title = element_blank()) + 
    labs(y = bquote("ug C" ~ L^{
        -1
    } ~ day^{
        -1
    }), x = ""), nrow = 3, labels = c("a", "b", "c"))
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

# dev.off() str(gr_env)

13 Excess

13.1 Explore putative enrichment

load("data-input/GR-countinfo-withASVdistribution.RData", verbose = T)
## Loading objects:
##   gr_stats_wtax_toplot
##   gr_stats_wtax
##   gr_dist_grazing
##   gr_dist
gr_stats_wtax_wgrazing <- gr_stats_wtax %>% left_join(gr_dist_grazing, by = "Feature.ID") %>% 
    filter(Graze_enriched == "Enriched")
# dim(gr_stats_wtax) dim(gr_stats_wtax_wgrazing)

# Focuses down to just over 1000 ASVs
length(unique(gr_stats_wtax$Feature.ID))
## [1] 9028
length(unique(gr_stats_wtax_wgrazing$Feature.ID))
## [1] 1031
# 
unique(gr_stats_wtax_wgrazing$LocationName)
## [1] "Deep seawater"     "Shallow seawater"  "Near vent BW"     
## [4] "Candelabra Plume"  "Mt Edwards Plume"  "Venti Latte Vent" 
## [7] "SirVentsAlot Vent" "Mt Edwards Vent"   "Candelabra Vent"
# head(gr_stats_wtax_wgrazing)
# Function to select ASVs of interest
dfnear <- gr_stats_wtax_wgrazing %>% type.convert(as.is = TRUE) %>%
  filter(near_graze == "near") %>% 
  filter(LocationName == "Near vent BW") %>% 
  filter(SAMPLEID != "T24") %>% #Remove T1, which was not used for the grazing calculations
  select(Feature.ID, LocationName, SAMPLEID, Kingdom:Species, Taxa, DIST_simple, COUNT_AVG) %>% 
  pivot_wider(names_from = SAMPLEID, values_from = COUNT_AVG, values_fill = 0) %>%
  mutate(t0 = case_when(
    T0 > sterivex ~ "higher",
    TRUE ~ "lower")) %>%
  mutate(tf = case_when(
      T36 > T0 ~ "higher",
      TRUE ~ "lower"
    )) %>% 
  # filter(DIST_simple == "Resident") %>% 
  # filter(tf == "higher") %>% 
  ##CHANGE x AXIS TO NUMERICS
  pivot_longer(cols = c(sterivex, T0, T36), names_to = "samples") %>% 
  mutate(x_num = case_when(
    samples == "sterivex" ~ 1,
    samples == "T0" ~ 2,
    TRUE ~ 3
  )) %>% 
  group_by(x_num, samples, LocationName, Taxa) %>% 
    summarise(SUM = sum(value),
              RICH = n_distinct(Feature.ID)) %>%
  data.frame
## `summarise()` regrouping output by 'x_num', 'samples', 'LocationName' (override with `.groups` argument)
#
# unique(gr_stats_wtax_wgrazing %>% filter(latte_graze == "latte") %>% 
  # filter(LocationName == "Venti Latte Vent"))
# unique(gr_stats_wtax_wgrazing$SAMPLEID)
dflatte <- gr_stats_wtax_wgrazing %>% type.convert(as.is = TRUE) %>%
  filter(latte_graze == "latte") %>% 
  filter(LocationName == "Venti Latte Vent") %>% 
# filter(SAMPLEID != "T24") %>% #Remove T1, which was not used for the grazing calculations
  # select(Feature.ID, LocationName, SAMPLEID, Kingdom:Species, Taxa, DIST_simple, COUNT_AVG) %>% 
  # Average across in situ replicates for Venti Latte
  group_by(Feature.ID, LocationName, SAMPLEID, Taxon_updated, Taxa, DIST_simple, COUNT_AVG) %>% 
  summarise(COUNT_AVG_AVG = mean(COUNT_AVG)) %>% 
  pivot_wider(names_from = SAMPLEID, values_from = COUNT_AVG_AVG, values_fill = 0) %>%
  mutate(t0 = case_when(
    T0 > SUPR ~ "higher",
    TRUE ~ "lower")) %>%
  mutate(tf = case_when(
      T36 > T0 ~ "higher",
      TRUE ~ "lower"
    )) %>%
  # filter(DIST_simple == "Resident") %>%
  # filter(tf == "higher") %>%
  ##CHANGE x AXIS TO NUMERICS
  pivot_longer(cols = c(SUPR, T0, T36), names_to = "samples") %>%
  mutate(x_num = case_when(
    samples == "SUPR" ~ 1,
    samples == "T0" ~ 2,
    TRUE ~ 3
  )) %>%
  group_by(x_num, samples, LocationName, Taxa) %>%
    summarise(SUM = sum(value),
              RICH = n_distinct(Feature.ID)) %>%
  data.frame
## `summarise()` regrouping output by 'Feature.ID', 'LocationName', 'SAMPLEID', 'Taxon_updated', 'Taxa', 'DIST_simple' (override with `.groups` argument)
## `summarise()` regrouping output by 'x_num', 'samples', 'LocationName' (override with `.groups` argument)
# head(dflatte)


dfsir <- gr_stats_wtax_wgrazing %>% type.convert(as.is = TRUE) %>%
  filter(sirvents_graze == "sirvents") %>% 
  filter(LocationName == "SirVentsAlot Vent") %>% 
  # filter(SAMPLEID != "T24") %>% #Remove T1, which was not used for the grazing calculations
  # select(Feature.ID, LocationName, SAMPLEID, Kingdom:Species, Taxa, DIST_simple, COUNT_AVG) %>% 
  # Average across in situ replicates for Venti Latte
  group_by(Feature.ID, LocationName, SAMPLEID, Taxon_updated, Taxa, DIST_simple, COUNT_AVG) %>% 
  summarise(COUNT_AVG_AVG = mean(COUNT_AVG)) %>% 
  pivot_wider(names_from = SAMPLEID, values_from = COUNT_AVG_AVG, values_fill = 0) %>%
  # mutate(t0 = case_when(
  #   T0 > SUPR ~ "higher",
  #   TRUE ~ "lower")) %>%
  mutate(tf = case_when(
      T24 > SUPR ~ "higher",
      TRUE ~ "lower"
    )) %>%
  # filter(DIST_simple == "Resident") %>%
  # filter(tf == "higher") %>%
  ##CHANGE x AXIS TO NUMERICS
  pivot_longer(cols = c(SUPR, T24), names_to = "samples") %>%
  mutate(x_num = case_when(
    samples == "SUPR" ~ 1,
    samples == "T24" ~ 2,
    TRUE ~ 3
  )) %>%
  group_by(x_num, samples, LocationName, Taxa) %>%
    summarise(SUM = sum(value),
              RICH = n_distinct(Feature.ID)) %>%
  data.frame
## `summarise()` regrouping output by 'Feature.ID', 'LocationName', 'SAMPLEID', 'Taxon_updated', 'Taxa', 'DIST_simple' (override with `.groups` argument)
## `summarise()` regrouping output by 'x_num', 'samples', 'LocationName' (override with `.groups` argument)
# head(dfsir)

# View(gr_stats_wtax_wgrazing %>%
#   filter(edwards_graze == "edwards") %>% 
#   filter(LocationName == "Mt Edwards Vent"))
# unique(gr_stats_wtax_wgrazing$edwards_graze)
dfed <- gr_stats_wtax_wgrazing %>% type.convert(as.is = TRUE) %>%
  filter(edwards_graze == "edwards") %>% 
  filter(LocationName == "Mt Edwards Vent") %>% 
  # filter(SAMPLEID != "T24") %>% #Remove T1, which was not used for the grazing calculations
  # select(Feature.ID, LocationName, SAMPLEID, Kingdom:Species, Taxa, DIST_simple, COUNT_AVG) %>% 
  # Average across in situ replicates for Venti Latte
  group_by(Feature.ID, LocationName, SAMPLEID, Taxon_updated, Taxa, DIST_simple, COUNT_AVG) %>% 
  summarise(COUNT_AVG_AVG = mean(COUNT_AVG)) %>% 
  pivot_wider(names_from = SAMPLEID, values_from = COUNT_AVG_AVG, values_fill = 0) %>%
  mutate(t0 = case_when(
    T0 > SUPR ~ "higher",
    TRUE ~ "lower")) %>%
  mutate(tf = case_when(
      T36 > SUPR ~ "higher",
      TRUE ~ "lower"
    )) %>%
  # filter(DIST_simple == "Resident") %>%
  # filter(tf == "higher") %>%
  ##CHANGE x AXIS TO NUMERICS
  pivot_longer(cols = c(SUPR, T0, T36), names_to = "samples") %>%
  mutate(x_num = case_when(
    samples == "SUPR" ~ 1,
    samples == "T0" ~ 2,
    TRUE ~ 3
  )) %>%
  group_by(x_num, samples, LocationName, Taxa) %>%
    summarise(SUM = sum(value),
              RICH = n_distinct(Feature.ID)) %>%
  data.frame
## `summarise()` regrouping output by 'Feature.ID', 'LocationName', 'SAMPLEID', 'Taxon_updated', 'Taxa', 'DIST_simple' (override with `.groups` argument)
## `summarise()` regrouping output by 'x_num', 'samples', 'LocationName' (override with `.groups` argument)
# head(dfed)
level2ORDER <- c("Alveolata-Ciliates", "Alveolata-Dinoflagellates", "Alveolata-Syndiniales", 
    "Alveolata-Other", "Rhizaria-Cercozoa", "Rhizaria-Radiolaria", "Rhizaria-Other", 
    "Stramenopiles-MAST", "Stramenopiles-Ochrophyta", "Stramenopiles-Other", "Hacrobia-Cryptophyta", 
    "Hacrobia-Haptophyta", "Hacrobia-Other", "Amoebozoa", "Excavata", "Apusozoa", 
    "Archaeplastida-Chlorophyta", "Archaeplastida-Other", "Opisthokonta-Fungi", "Opisthokonta-Metazoa", 
    "Opisthokonta-Other", "Unassigned-Eukaryote")

level2color <- c("#f1eef6", "#d7b5d8", "#df65b0", "#ce1256", "#fc9272", "#ef3b2c", 
    "#800026", "#fff7bc", "#fec44f", "#d95f0e", "#74c476", "#238b45", "#00441b", 
    "#7fcdbb", "#084081", "#c6dbef", "#2b8cbe", "#016c59", "#bcbddc", "#807dba", 
    "#54278f", "#bdbdbd")

names(level2color) <- level2ORDER

# FACTORING:
dfnear$LEVEL2ORDER <- factor(dfnear$Taxa, levels = level2ORDER)
dflatte$LEVEL2ORDER <- factor(dflatte$Taxa, levels = level2ORDER)
dfsir$LEVEL2ORDER <- factor(dfsir$Taxa, levels = level2ORDER)
dfed$LEVEL2ORDER <- factor(dfed$Taxa, levels = level2ORDER)
# 
nearbw <- ggplot(dfnear, aes(x = x_num, fill = LEVEL2ORDER, y = SUM)) + geom_area(aes(fill = LEVEL2ORDER), 
    position = "stack", stat = "identity", color = "black") + scale_x_discrete(expand = c(0, 
    0)) + scale_fill_manual(values = level2color) + annotate(geom = "text", x = 1.1, 
    y = -0.02, label = "in situ", color = "black") + annotate(geom = "text", x = 2, 
    y = -0.02, label = "T0", color = "black") + annotate(geom = "text", x = 2.9, 
    y = -0.02, label = "TF", color = "black") + theme(legend.position = "right", 
    panel.border = element_blank(), panel.background = element_blank(), axis.text.x = element_blank(), 
    axis.ticks.x = element_line(), axis.text.y = element_text(color = "black", face = "bold"), 
    legend.title = element_blank()) + labs(x = "Near vent BW", y = "Relative abundance")
# nearbw
venti <- ggplot(dflatte, aes(x = x_num, fill = LEVEL2ORDER, y = SUM)) + geom_area(aes(fill = LEVEL2ORDER), 
    position = "stack", stat = "identity", color = "black") + scale_x_discrete(expand = c(0, 
    0)) + scale_fill_manual(values = level2color) + # scale_color_manual(values = level2color) +
annotate(geom = "text", x = 1.1, y = -0.02, label = "in situ", color = "black") + 
    annotate(geom = "text", x = 2, y = -0.02, label = "T0", color = "black") + annotate(geom = "text", 
    x = 2.9, y = -0.02, label = "TF", color = "black") + theme(legend.position = "right", 
    panel.border = element_blank(), panel.background = element_blank(), axis.text.x = element_blank(), 
    axis.ticks.x = element_line(), axis.text.y = element_text(color = "black", face = "bold"), 
    legend.title = element_blank()) + labs(x = "Venti latte", y = "Relative abundance")
# venti
sirvent <- ggplot(dfsir, aes(x = x_num, fill = LEVEL2ORDER, y = SUM)) + geom_area(aes(fill = LEVEL2ORDER), 
    position = "stack", stat = "identity", color = "black") + scale_x_discrete(expand = c(0, 
    0)) + scale_fill_manual(values = level2color) + # scale_color_manual(values = level2color) +
annotate(geom = "text", x = 1.1, y = -0.02, label = "in situ", color = "black") + 
    annotate(geom = "text", x = 2, y = -0.02, label = "TF", color = "black") + # annotate(geom='text', x=2.9, y=-0.02, label='TF', color='black') +
theme(legend.position = "right", panel.border = element_blank(), panel.background = element_blank(), 
    axis.text.x = element_blank(), axis.ticks.x = element_line(), axis.text.y = element_text(color = "black", 
        face = "bold"), legend.title = element_blank()) + labs(x = "Sir Ventsalot", 
    y = "Relative abundance")
# sirvent
edward <- ggplot(dfed, aes(x = x_num, fill = LEVEL2ORDER, y = SUM)) + geom_area(aes(fill = LEVEL2ORDER), 
    position = "stack", stat = "identity", color = "black") + scale_x_discrete(expand = c(0, 
    0)) + scale_fill_manual(values = level2color) + # scale_color_manual(values = level2color) +
annotate(geom = "text", x = 1.1, y = -0.02, label = "in situ", color = "black") + 
    annotate(geom = "text", x = 2, y = -0.02, label = "TF", color = "black") + annotate(geom = "text", 
    x = 2.9, y = -0.02, label = "TF", color = "black") + theme(legend.position = "right", 
    panel.border = element_blank(), panel.background = element_blank(), axis.text.x = element_blank(), 
    axis.ticks.x = element_line(), axis.text.y = element_text(color = "black", face = "bold"), 
    legend.title = element_blank()) + labs(x = "Sir Ventsalot", y = "Relative abundance")
# edward

14 Session Info

sessionInfo()
## R version 3.6.1 (2019-07-05)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: macOS Mojave 10.14.6
## 
## Matrix products: default
## BLAS/LAPACK: /Users/sarahhu/anaconda3/envs/r_3.6.0/lib/R/lib/libRblas.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] broom_0.7.0          ggalluvial_0.12.1    dendextend_1.14.0   
##  [4] ggdendro_0.1-20      ape_5.3              RColorBrewer_1.1-2  
##  [7] cluster_2.1.0        compositions_1.40-5  bayesm_3.1-4        
## [10] robustbase_0.93-6    tensorA_0.36.1       ade4_1.7-15         
## [13] vegan_2.5-6          lattice_0.20-41      permute_0.9-5       
## [16] decontam_1.6.0       phyloseq_1.30.0      patchwork_1.0.0.9000
## [19] cowplot_1.0.0        reshape2_1.4.4       forcats_0.5.0       
## [22] stringr_1.4.0        dplyr_1.0.0          purrr_0.3.4         
## [25] readr_1.3.1          tidyr_1.1.0          tibble_3.0.1        
## [28] ggplot2_3.3.1        tidyverse_1.3.0     
## 
## loaded via a namespace (and not attached):
##  [1] VGAM_1.1-3          colorspace_1.4-1    ellipsis_0.3.1     
##  [4] XVector_0.26.0      fs_1.4.1            rstudioapi_0.11    
##  [7] farver_2.0.3        fansi_0.4.1         lubridate_1.7.8    
## [10] xml2_1.3.2          codetools_0.2-16    splines_3.6.1      
## [13] knitr_1.28          SpiecEasi_1.1.0     jsonlite_1.6.1     
## [16] dbplyr_1.4.4        compiler_3.6.1      httr_1.4.1         
## [19] backports_1.1.7     assertthat_0.2.1    Matrix_1.2-18      
## [22] cli_2.0.2           formatR_1.7         htmltools_0.4.0    
## [25] tools_3.6.1         igraph_1.2.5        gtable_0.3.0       
## [28] glue_1.4.1          Rcpp_1.0.5          Biobase_2.46.0     
## [31] cellranger_1.1.0    vctrs_0.3.0         Biostrings_2.54.0  
## [34] multtest_2.42.0     nlme_3.1-148        iterators_1.0.12   
## [37] xfun_0.14           rvest_0.3.5         lifecycle_0.2.0    
## [40] DEoptimR_1.0-8      zlibbioc_1.32.0     MASS_7.3-51.6      
## [43] scales_1.1.1        hms_0.5.3           parallel_3.6.1     
## [46] biomformat_1.14.0   huge_1.3.4.1        rhdf5_2.30.1       
## [49] yaml_2.2.1          gridExtra_2.3       stringi_1.4.6      
## [52] S4Vectors_0.24.4    foreach_1.5.0       BiocGenerics_0.32.0
## [55] shape_1.4.4         rlang_0.4.6         pkgconfig_2.0.3    
## [58] evaluate_0.14       Rhdf5lib_1.8.0      labeling_0.3       
## [61] tidyselect_1.1.0    plyr_1.8.6          magrittr_1.5       
## [64] R6_2.4.1            IRanges_2.20.2      generics_0.0.2     
## [67] DBI_1.1.0           pillar_1.4.4        haven_2.3.1        
## [70] withr_2.2.0         mgcv_1.8-31         survival_3.1-12    
## [73] pulsar_0.3.7        modelr_0.1.8        crayon_1.3.4       
## [76] rmarkdown_2.2       viridis_0.5.1       grid_3.6.1         
## [79] readxl_1.3.1        data.table_1.12.8   blob_1.2.1         
## [82] reprex_0.3.0        digest_0.6.25       stats4_3.6.1       
## [85] munsell_0.5.0       glmnet_4.0-2        viridisLite_0.3.0